diff --git a/.gitignore b/.gitignore
index 33e7851..9d913f1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,7 +3,7 @@
__pycache__/
*.py[cod]
*$py.class
-
+.idea/
# C extensions
*.so
diff --git a/_reference/LICENSE b/_reference/LICENSE
new file mode 100644
index 0000000..5a3eac4
--- /dev/null
+++ b/_reference/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 talshapira
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/_reference/README.md b/_reference/README.md
new file mode 100644
index 0000000..d5cb68d
--- /dev/null
+++ b/_reference/README.md
@@ -0,0 +1,67 @@
+# FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification
+
+Identifying the type of a network flow or a specific application has many advantages, but become harder in recent years due to the use of encryption, e.g., by VPN and Tor.
+Current solutions rely mostly on handcrafted features and then apply supervised learning techniques for the classification.
+
+We introduce a novel approach for encrypted Internet traffic classification and application identification by transforming basic flow data into a picture, em a FlowPic, and then using known image classification deep learning techniques, Convolutional Neural Networks (CNNs), to identify the flow category (browsing, chat, video, etc.) and the application in use. Our approach can classify traffic with high accuracy, both for a specific application, or a flow category, even for VPN and Tor traffic. Our classifier can even identify with high success new applications that were not part of the training phase for a category, thus, new versions or applications can be categorized without additional training.
+
+A recent [work](https://arxiv.org/abs/2104.03182) by Yang et al. compared different recent methods for Internet Traffic Classification, and showed that our method achieves the best tradeoff between accuracy and model complexity, as shown below (FlowPic marked with [17]):
+
+
+
+
+
+# Approach
+
+1. Extract records from each flow, which comprised of a list of pairs, {IP packet size, time of arrival} for each packet in the flow.
+2. Split each unidirectional flow to equal blocks (15/60 seconds).
+3. Generate 2D-histogram. For simplicity, we set the 2D-histogram to be a square image.
+4. Feed a Convolution Neural Network.
+
+
+
+
+# FlowPics Exploration
+
+
+
+
+
+
+
+# Dataset
+
+We use labeled datasets of packet capture (pcap) files from the Uni. of New Brunswick (UNB): ["ISCX VPN-nonVPN traffic dataset" (ISCX-VPN)](https://www.unb.ca/cic/datasets/vpn.html) and ["ISCX Tor-nonTor dataset" (ISCX-Tor)](https://www.unb.ca/cic/datasets/tor.html), as well as our own small packet capture (TAU), and conduct different types of experiments; (1) multiclass classification experiments over non-VPN/VPN/Tor and merged dataset, (2) class vs. all classification experiments, (3) application identification, and (4) classification of an unknown application.
+
+Each pcap file corresponds to a specific application, a traffic category and an encryption technique. However, all these captures also contain sessions of different traffic categories, since while performing one action in an application, many other sessions occur for different tasks simultaneously. For example, while using VoIP over Facebook, there is another STUN session taking place at the same time for adjusting and maintaining the VoIP conversation, as well as an HTTPS session of the Facebook site.
+
+We use a combined dataset only from the five categories that contains enough samples: VoIP, Video, Chat, Browsing, and File Transfer. For these categories we have 3 encryption techniques: non VPN, VPN (for all classes except Browsing) and TOR.
+Notice that our categories differ slightly from those suggested by UNB. All the applications that were captured in order to create the dataset, for each traffic category and encryption technique, are shown in the folowing table:
+
+
+
+
+
+We parsed the pcap files and constructed for each combination of traffic category and encryption technique a CSV file with the following structure -
+|pcap_name|ip_src|port_src|ip_dst|port_dst|TCP/UDP|start_time|length|[timestamps_list]|[sizes_list]| , such that each entry corresponds to a specific unidirectional session.
+
+# TrafficParser
+
+Contains the code use to generate the dataset (npy files) per experiment.
+If you choose to use our proceesed dataset (i.e. CSV files) directly, run the scripts in the following order:
+1. Run traffic_csv_converter.py
+2. Run datasets_generator.py
+
+The other two scripts (generic_parser.py + traffic_csv_merger.py) used to generate the proceesed dataset.
+
+# License
+
+Our proceesed dataset (i.e. CSV files) is [publicly available](https://drive.google.com/file/d/1gz61vnMANj-4hKNvZv1KFK9LajR91X-m/view?usp=sharing) upon request for researchers. If you are using our dataset, please cite our related research paper, as well as UNB's related research papers:
+
+* T. Shapira and Y. Shavitt, "FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification," in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2021.3071441.
+
+* T. Shapira and Y. Shavitt, "FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition," IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France, 2019, pp. 680-687.
+
+* Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016) , pages 407-414, Rome, Italy.
+
+* Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun and Ali A. Ghorbani, "Characterization of Tor Traffic Using Time Based Features", In the proceeding of the 3rd International Conference on Information System Security and Privacy, SCITEPRESS, Porto, Portugal, 2017.
diff --git a/_reference/TrafficParser/datasets_generator.py b/_reference/TrafficParser/datasets_generator.py
new file mode 100644
index 0000000..586aaef
--- /dev/null
+++ b/_reference/TrafficParser/datasets_generator.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+"""
+datasets_generator.py creates final class_vs_all dataset ready to be inserted to machine.
+The input for this module are pre-created numpy array containing all classes session 2d_histograms created in traffic_csv_conveter.py
+"""
+import glob
+import numpy as np
+# from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
+
+CLASS = "browsing"
+TEST_SIZE = 0.1
+DATASET_DIR = "../datasets/"
+
+VPN_TYPES = {
+ "reg": glob.glob("../raw_csvs/classes/**/reg/*.npy"),
+ "vpn": glob.glob("../raw_csvs/classes/**/vpn/*.npy"),
+ "tor": glob.glob("../raw_csvs/classes/**/tor/*.npy")
+}
+
+
+def import_array(input_array):
+ print("Import dataset " + input_array)
+ dataset = np.load(input_array)
+ print(dataset.shape)
+ return dataset
+
+
+def export_dataset(dataset_dict, file_path):
+ # with open(file_path + ".pkl", 'wb') as outfile:
+ # pickle.dump(dataset_list, outfile, pickle.HIGHEST_PROTOCOL)
+ for name, array in dataset_dict.items():
+ np.save(file_path + "_" + name, array)
+
+
+def create_class_vs_all_specific_vpn_type_dataset(class_name, vpn_type="reg", validation=False, ratio=1.2):
+ class_array_file = [fn for fn in VPN_TYPES[vpn_type] if class_name in fn and "overlap" not in fn][0]
+ print(class_array_file)
+ all_files = [fn for fn in VPN_TYPES[vpn_type] if class_name not in fn and "overlap" not in fn]
+ print(all_files)
+
+ class_array = import_array(class_array_file)
+ count = len(class_array)
+ print(count)
+
+ all_count = len(all_files)
+ count_per_class = ratio*count/all_count
+ print(count_per_class)
+
+ for fn in all_files:
+ print(fn)
+ fn_array = import_array(fn)
+ p = count_per_class*1.0/len(fn_array)
+ print(p)
+ if p < 1:
+ mask = np.random.choice([True, False], len(fn_array), p=[p, 1-p])
+ fn_array = fn_array[mask]
+
+ print(len(fn_array))
+ class_array = np.append(class_array, fn_array, axis=0)
+ print(len(class_array))
+ del fn_array
+
+ labels = np.append(np.zeros(count), np.ones(len(class_array) - count))
+ print(len(class_array), len(labels), labels[0], labels[count-1], labels[count], labels[-1])
+ dataset_dict = dict()
+
+ if validation:
+ x_train, x_val, y_train, y_val = train_test_split(class_array, labels, test_size=TEST_SIZE)
+ print(len(y_train), sum(y_train), 1.0*sum(y_train)/len(y_train))
+ print(len(y_val), sum(y_val), 1.0*sum(y_val)/len(y_val))
+
+ dataset_dict["x_train"] = x_train
+ dataset_dict["x_val"] = x_val
+ dataset_dict["y_train"] = y_train
+ dataset_dict["y_val"] = y_val
+ else:
+ dataset_dict["x_test"] = class_array
+ dataset_dict["y_test"] = labels
+
+ export_dataset(dataset_dict, DATASET_DIR + class_name + "_vs_all_" + vpn_type)
+
+
+if __name__ == '__main__':
+ # create_class_vs_all_specific_vpn_type_dataset(CLASS, validation=True)
+ # create_class_vs_all_specific_vpn_type_dataset(CLASS, vpn_type="vpn", validation=False)
+ create_class_vs_all_specific_vpn_type_dataset(CLASS, vpn_type="tor", validation=False)
diff --git a/_reference/TrafficParser/generic_parser.py b/_reference/TrafficParser/generic_parser.py
new file mode 100644
index 0000000..c1a15e4
--- /dev/null
+++ b/_reference/TrafficParser/generic_parser.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+"""
+Use DPKT to read in a pcap file and create one directional sessions of packets sizes (ip total length) and ts.
+"""
+import dpkt
+import os
+import socket
+import argparse
+import csv
+import time
+
+FLAGS = None
+# INPUT = "../dataset/iscxNTVPN2016/CompletePCAPs"#"../dataset/CICNTTor2017/Pcaps/tor" #"../dataset/iscxNTVPN2016/CompletePCAPs"#"./test_pacaps"#"../dataset/iscxNTVPN2016/CompletePCAPs" # ""
+INPUT = './test_pcaps/my_chat'
+FILTER_LIST = None # [(["audio", "voip"], True), (["vpn", "tor"], False)]
+
+PROTO_DICT = {dpkt.tcp.TCP: "TCP", dpkt.udp.UDP: "UDP"}
+
+
+def inet_to_str(inet):
+ """Convert inet object to a string
+ Args:
+ inet (inet struct): inet network address
+ Returns:
+ str: Printable/readable IP address
+ """
+ # First try ipv4 and then ipv6
+ try:
+ return socket.inet_ntop(socket.AF_INET, inet)
+ except ValueError:
+ return socket.inet_ntop(socket.AF_INET6, inet)
+
+
+def get_pcaps_list(dir_path, filter_list=None):
+ def filter_list_func(fn):
+ if filter_list is not None:
+ for filter_str_list, type in filter_list:
+ result = any([filter_str in fn.lower() for filter_str in filter_str_list])
+ if result is not type:
+ return False
+ return True
+ return [(os.path.join(dir_path, fn), fn) for fn in next(os.walk(dir_path))[2] if (".pcap" in os.path.splitext(fn)[-1] and filter_list_func(fn))]
+
+
+def parse_pcap(pcap, pcap_path, file_name):
+ """Print out information about each packet in a pcap
+ Args:
+ pcap: dpkt pcap reader object (dpkt.pcap.Reader)
+ """
+ counter = 0
+ pcap_dict = {}
+
+ # For each packet in the pcap process the contents
+ for ts, packet in pcap:
+
+ # Unpack the Ethernet frame
+ try:
+ eth = dpkt.ethernet.Ethernet(packet)
+ except dpkt.dpkt.NeedData:
+ print("dpkt.dpkt.NeedData")
+
+ # Make sure the Ethernet data contains an IP packet
+ if isinstance(eth.data, dpkt.ip.IP):
+ ip = eth.data
+ elif isinstance(eth.data, str):
+ try:
+ ip = dpkt.ip.IP(packet)
+ except dpkt.UnpackError:
+ continue
+ else:
+ continue
+
+ # Now unpack the data within the Ethernet frame (the IP packet)
+ # Pulling out src_ip, dst_ip, protocol (tcp/udp), dst/src port, length
+
+ proto = ip.data
+
+ # Print out the info
+ if type(ip.data)in PROTO_DICT:
+ session_tuple_key = (inet_to_str(ip.src), proto.sport, inet_to_str(ip.dst), proto.dport, PROTO_DICT[type(ip.data)])
+ pcap_dict.setdefault(session_tuple_key, (ts, [], []))
+ d = pcap_dict[session_tuple_key]
+ size = len(ip) #ip.len
+ d[1].append(round(ts - d[0], 6)), d[2].append(size)
+ counter += 1
+
+ print("Total Number of Parsed Packets in " + pcap_path + ": " + str(counter))
+
+ csv_file_path = os.path.splitext(pcap_path)[0] + ".csv"
+ with open(csv_file_path, 'wb') as csv_file:
+ writer = csv.writer(csv_file)
+ for key, value in pcap_dict.items():
+ writer.writerow([file_name.split(".")[0]] + list(key) + [value[0], len(value[1])] + value[1] + [None] + value[2])
+
+ for k,v in pcap_dict.iteritems():
+ if len(v[1]) > 2000:
+ print(k, v[0], len(v[1]))
+
+
+def generic_parser(file_list):
+ """Open up a pcap file and create a output file containing all one-directional parsed sessions"""
+ for pcap_path, file_name in file_list:
+ try:
+ with open(pcap_path, 'rb') as f:
+ pcap = dpkt.pcap.Reader(f)
+ parse_pcap(pcap, pcap_path, file_name)
+
+ except ValueError:
+ new_pcap_file = os.path.splitext(pcap_path)[0] + "_new.pcap"
+ os.system("editcap -F libpcap -T ether " + pcap_path + " " + new_pcap_file)
+
+ with open(new_pcap_file, 'rb') as f:
+ pcap = dpkt.pcap.Reader(f)
+ parse_pcap(pcap, pcap_path, file_name)
+
+ os.remove(new_pcap_file)
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', type=str, default=INPUT, help='Path to pcap')
+
+ FLAGS = parser.parse_args()
+ file_list = get_pcaps_list(FLAGS.input, FILTER_LIST)
+ start_time = time.time()
+ generic_parser(file_list)
+ total_time = time.time() - start_time
+ print("--- %s seconds ---" % total_time)
diff --git a/_reference/TrafficParser/sessions_plotter.py b/_reference/TrafficParser/sessions_plotter.py
new file mode 100644
index 0000000..d4bc409
--- /dev/null
+++ b/_reference/TrafficParser/sessions_plotter.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+"""
+sessions_plotter.py has 3 functions to create spectogram, histogram, 2d_histogram from [(ts, size),..] session.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+MTU = 1500
+
+def session_spectogram(ts, sizes, name=None):
+ plt.scatter(ts, sizes, marker='.')
+ plt.ylim(0, MTU)
+ plt.xlim(ts[0], ts[-1])
+ # plt.yticks(np.arange(0, MTU, 10))
+ # plt.xticks(np.arange(int(ts[0]), int(ts[-1]), 10))
+ plt.title(name + " Session Spectogram")
+ plt.ylabel('Size [B]')
+ plt.xlabel('Time [sec]')
+
+ plt.grid(True)
+ plt.show()
+
+
+def session_atricle_spectogram(ts, sizes, fpath=None, show=True, tps=None):
+ if tps is None:
+ max_delta_time = ts[-1] - ts[0]
+ else:
+ max_delta_time = tps
+
+ ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
+ plt.figure()
+ plt.scatter(ts_norm, sizes, marker=',', c='k', s=5)
+ plt.ylim(0, MTU)
+ plt.xlim(0, MTU)
+ plt.ylabel('Packet Size [B]')
+ plt.xlabel('Normalized Arrival Time')
+ plt.set_cmap('binary')
+ plt.axes().set_aspect('equal')
+ plt.grid(False)
+ if fpath is not None:
+ # plt.savefig(OUTPUT_DIR + fname, bbox_inches='tight', pad_inches=1)
+ plt.savefig(fpath, bbox_inches='tight')
+ if show:
+ plt.show()
+ plt.close()
+
+
+def session_histogram(sizes, plot=False):
+ hist, bin_edges = np.histogram(sizes, bins=range(0, MTU + 1, 1))
+ if plot:
+ plt.bar(bin_edges[:-1], hist, width=1)
+ plt.xlim(min(bin_edges), max(bin_edges)+100)
+ plt.show()
+ return hist.astype(np.uint16)
+
+
+def session_2d_histogram(ts, sizes, plot=False, tps=None):
+ if tps is None:
+ max_delta_time = ts[-1] - ts[0]
+ else:
+ max_delta_time = tps
+
+ # ts_norm = map(int, ((np.array(ts) - ts[0]) / max_delta_time) * MTU)
+ ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
+ H, xedges, yedges = np.histogram2d(sizes, ts_norm, bins=(range(0, MTU + 1, 1), range(0, MTU + 1, 1)))
+
+ if plot:
+ plt.pcolormesh(xedges, yedges, H)
+ plt.colorbar()
+ plt.xlim(0, MTU)
+ plt.ylim(0, MTU)
+ plt.set_cmap('binary')
+ plt.show()
+ return H.astype(np.uint16)
diff --git a/_reference/TrafficParser/traffic_csv_converter.py b/_reference/TrafficParser/traffic_csv_converter.py
new file mode 100644
index 0000000..1d83f25
--- /dev/null
+++ b/_reference/TrafficParser/traffic_csv_converter.py
@@ -0,0 +1,197 @@
+#!/usr/bin/env python
+"""
+Read traffic_csv
+"""
+
+import os
+import argparse
+import csv
+from sessions_plotter import *
+import glob
+import re
+
+FLAGS = None
+INPUT = "../raw_csvs/classes/browsing/reg/CICNTTor_browsing.raw.csv"#"../dataset/iscxNTVPN2016/CompletePCAPs" # ""
+INPUT_DIR = "../raw_csvs/classes/chat/vpn/"
+CLASSES_DIR = "../raw_csvs/classes/**/**/"
+
+# LABEL_IND = 1
+TPS = 60 # TimePerSession in secs
+DELTA_T = 60 # Delta T between splitted sessions
+MIN_TPS = 50
+
+# def insert_dataset(dataset, labels, session, label_ind=LABEL_IND):
+# dataset.append(session)
+# labels.append(label_ind)
+
+# def export_dataset(dataset, labels):
+# print "Start export dataset"
+# np.savez(INPUT.split(".")[0] + ".npz", X=dataset, Y=labels)
+# print dataset.shape, labels.shape
+
+#
+# def import_dataset():
+# print "Import dataset"
+# dataset = np.load(INPUT.split(".")[0] + ".npz")
+# print dataset["X"].shape, dataset["Y"].shape
+
+
+def export_dataset(dataset):
+ print("Start export dataset")
+ np.save(os.path.splitext(INPUT)[0], dataset)
+ print(dataset.shape)
+
+
+def export_class_dataset(dataset, class_dir):
+ print("Start export dataset")
+ np.save(class_dir + "/" + "_".join(re.findall(r"[\w']+", class_dir)[-2:]), dataset)
+ print(dataset.shape)
+
+
+def import_dataset():
+ print("Import dataset")
+ dataset = np.load(os.path.splitext(INPUT)[0] + ".npy")
+ print(dataset.shape)
+ return dataset
+
+
+def traffic_csv_converter(file_path):
+ print("Running on " + file_path)
+ dataset = []
+ # labels = []
+ counter = 0
+ with open(file_path, 'r') as csv_file:
+ reader = csv.reader(csv_file)
+ for i, row in enumerate(reader):
+ # print row[0], row[7]
+ session_tuple_key = tuple(row[:8])
+ length = int(row[7])
+ ts = np.array(row[8:8+length], dtype=float)
+ sizes = np.array(row[9+length:], dtype=int)
+
+ # if (sizes > MTU).any():
+ # a = [(sizes[i], i) for i in range(len(sizes)) if (np.array(sizes) > MTU)[i]]
+ # print len(a), session_tuple_key
+
+ if length > 10:
+ # print ts[0], ts[-1]
+ # h = session_2d_histogram(ts, sizes)
+ # session_spectogram(ts, sizes, session_tuple_key[0])
+ # dataset.append([h])
+ # counter += 1
+ # if counter % 100 == 0:
+ # print counter
+
+ for t in range(int(ts[-1]/DELTA_T - TPS/DELTA_T) + 1):
+ mask = ((ts >= t * DELTA_T) & (ts <= (t * DELTA_T + TPS)))
+ # print t * DELTA_T, t * DELTA_T + TPS, ts[-1]
+ ts_mask = ts[mask]
+ sizes_mask = sizes[mask]
+ if len(ts_mask) > 10 and ts_mask[-1] - ts_mask[0] > MIN_TPS:
+ # if "facebook" in session_tuple_key[0]:
+ # session_spectogram(ts[mask], sizes[mask], session_tuple_key[0])
+ # # session_2d_histogram(ts[mask], sizes[mask], True)
+ # session_histogram(sizes[mask], True)
+ # exit()
+ # else:
+ # continue
+
+ h = session_2d_histogram(ts_mask, sizes_mask)
+ # session_spectogram(ts_mask, sizes_mask, session_tuple_key[0])
+ dataset.append([h])
+ counter += 1
+ if counter % 100 == 0:
+ print(counter)
+
+ return np.asarray(dataset) #, np.asarray(labels)
+
+
+def traffic_csv_converter_splitted(file_path):
+ def split_converter(ts, sizes, dataset, counter):
+ if ts[-1] - ts[0] > MIN_TPS and len(ts) > 20:
+ # print ts[0], ts[-1]
+ h = session_2d_histogram(ts-ts[0], sizes)
+ # session_spectogram(ts, sizes, session_tuple_key[0])
+ dataset.append([h])
+ counter += 1
+ # if counter % 100 == 0:
+ # print counter
+
+ total_time = ts[-1] - ts[0]
+ if total_time > TPS:
+ for ts_split, sizes_split in zip(np.split(ts, [len(ts)/2]), np.split(sizes, [len(sizes)/2])):
+ split_converter(ts_split, sizes_split, dataset, counter)
+
+ print("Running on " + file_path)
+ dataset = []
+ # labels = []
+ counter = 0
+ with open(file_path, 'r') as csv_file:
+ reader = csv.reader(csv_file)
+ for i, row in enumerate(reader):
+ # print row[0], row[7]
+ session_tuple_key = tuple(row[:8])
+ length = int(row[7])
+ ts = np.array(row[8:8+length], dtype=float)
+ sizes = np.array(row[9+length:], dtype=int)
+
+ # if (sizes > MTU).any():
+ # a = [(sizes[i], i) for i in range(len(sizes)) if (np.array(sizes) > MTU)[i]]
+ # print len(a), session_tuple_key
+
+ if length > 10:
+ split_converter(ts, sizes, dataset, counter)
+
+ return np.asarray(dataset)
+
+
+def traffic_class_converter(dir_path):
+ dataset_tuple = ()
+ for file_path in [os.path.join(dir_path, fn) for fn in next(os.walk(dir_path))[2] if (".csv" in os.path.splitext(fn)[-1])]:
+ dataset_tuple += (traffic_csv_converter(file_path),) ################
+
+ return np.concatenate(dataset_tuple, axis=0)
+
+
+def iterate_all_classes():
+ for class_dir in glob.glob(CLASSES_DIR):
+ if "other" not in class_dir: #"browsing" not in class_dir and
+ print("working on " + class_dir)
+ dataset = traffic_class_converter(class_dir)
+ print(dataset.shape)
+ export_class_dataset(dataset, class_dir)
+
+
+def random_sampling_dataset(input_array, size=2000):
+ print("Import dataset " + input_array)
+ dataset = np.load(input_array)
+ print(dataset.shape)
+ p = size*1.0/len(dataset)
+ print(p)
+ if p >= 1:
+ raise Exception
+
+ mask = np.random.choice([True, False], len(dataset), p=[p, 1-p])
+ dataset = dataset[mask]
+ print("Start export dataset")
+
+ np.save(os.path.splitext(input_array)[0] + "_samp", dataset)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', type=str, default=INPUT, help='Path to csv file')
+
+ FLAGS = parser.parse_args()
+ ##
+ # iterate_all_classes()
+
+ # dataset = traffic_class_converter(INPUT_DIR)
+ # dataset = traffic_csv_converter(INPUT)
+
+ input_array = "../raw_csvs/classes/browsing/reg/browsing_reg.npy"
+ random_sampling_dataset(input_array)
+
+
+ # export_class_dataset(dataset)
+ # import_dataset()
diff --git a/_reference/TrafficParser/traffic_csv_merger.py b/_reference/TrafficParser/traffic_csv_merger.py
new file mode 100644
index 0000000..fc78477
--- /dev/null
+++ b/_reference/TrafficParser/traffic_csv_merger.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+"""
+traffic_csv_merger.py merge all filtered traffic_csvs from the same original_pacps_dataest, class and vpn type into one merged csv file.
+"""
+
+import os
+import argparse
+import csv
+from sessions_plotter import *
+
+FLAGS = None
+
+# INPUT = "../raw_csvs/CICNTTor2017/tor"
+# INPUT = "../raw_csvs/iscxNTVPN2016" # ""
+# INPUT = "D:/TS/Internet Traffic Classification/TrafficParser/test_pcaps/my_chat"
+INPUT = "./test_pcaps/my_chat"
+# OUTPUT1 = "CICNTTor_browsing_tor.raw.csv"
+# OUTPUT2 = "CICNTTor_browsing_others_tor.raw.csv"
+# OUTPUT1 = "iscx_email.raw.csv"
+# OUTPUT2 = "iscx_email_others.raw.csv"
+# OUTPUT3 = "iscx_video_voip.raw.csv"
+OUTPUT1 = "my_chat.raw.csv"
+OUTPUT2 = "my_chat_others.raw.csv"
+
+# FILTER_LIST = [(["audio", "voip"], True), (["tor", "vpn"], False)] #-> voip, , "tor"
+# FILTER_LIST = [(["video", "youtube", "vimeo", "netflix"], True), (["tor", "vpn"], False)] #-> video
+# FILTER_LIST = [(["audio", "voip"], True), (["spotify"], False)] #-> voip, , "tor"
+# FILTER_LIST = [(["ftps", "scp", "sftp", "file"], True), (["mail", "pop", "tor"], False), (["vpn"], True)]
+FILTER_LIST = [(["chat"], True), (["vpn"], False)]
+
+
+def get_csvs_list(dir_path, filter_list=None):
+ def filter_list_func(fn):
+ if filter_list is not None:
+ for filter_str_list, type in filter_list:
+ result = any([filter_str in fn.lower() for filter_str in filter_str_list])
+ if result is not type:
+ return False
+ return True
+
+ return [(os.path.join(dir_path, fn), fn) for fn in next(os.walk(dir_path))[2] if (".csv" in os.path.splitext(fn)[-1] and filter_list_func(fn))]
+
+
+def traffic_csv_reader(file_list):
+
+ output1 = open(OUTPUT1, 'w')
+ writer1 = csv.writer(output1)
+ counter1 = 0
+ output2 = open(OUTPUT2, 'w')
+ writer2 = csv.writer(output2)
+ counter2 = 0
+ # output3 = open(OUTPUT3, 'wb')
+ # writer3 = csv.writer(output3)
+ # counter3 = 0
+
+ rate_list = []
+ for i, (file_path, file_name) in enumerate(file_list):
+ print("Running on " + str(i) + " file - " + file_path)
+ with open(file_path, 'r') as csv_file:
+ reader = csv.reader(csv_file)
+ for i, row in enumerate(reader):
+ session_tuple_key = tuple(row[:8])
+ length = int(row[7])
+ ts = np.array(row[8:8+length], dtype=float)
+ if length >= 20:
+ total_time = ts[-1] - ts[0]
+ sizes = np.array(row[9+length:], dtype=int)
+ # print row[0], length, total_time, length/total_time
+ rate = length/total_time
+ rate_list.append(rate)
+ # if (sizes > MTU).any():
+ # a = [(sizes[i], i) for i in range(len(sizes)) if (np.array(sizes) > MTU)[i]]
+ # print len(a), session_tuple_key, a
+
+ # if ("facebook" in row[0] and rate > 40) or ("facebook" not in row[0] and 20 <= rate and length >= 1000): # for iscx_voip
+ # if "facebook" not in row[0] and 40 <= rate and length >= 1000: # for iscx_voip_vpn
+ # if ("youtube" in row[0] and row[2] == '443' and total_time > 10 and rate > 15) or ("vimeo" in row[0] and rate > 30 and total_time > 15) or ("netflix" in row[0] and rate > 60) or ("facebook" in row[0] and rate > 60) or (40 <= rate and row[5] == "UDP" and "facebook" not in row[0]): # for iscx_video
+ # if length > 6000 and rate > 10 and total_time > 10 and (row[2] == '443' or row[2] == '80'): # for iscx_video_vpn
+ # if total_time > 30 and rate > 30 and (row[2] == '443' or row[2] == '80'): # for CICNTTor_video
+ # if total_time > 30 and (row[2] == '443' or row[2] == '80'): # for CICNTTor_video
+ # if total_time > 10 and rate > 10 and length > 1000 and (session_tuple_key[-1] != '3326' and session_tuple_key[-1] != '6367'): # for CICNTTor_voip
+ # if (total_time > 10 and ((rate > 100) or ("skype" in row[0] and rate > 10))) or ("rent" in row[0] and total_time > 20 and row[2] != '21943' and row[4] != '28904'): #for iscx_file
+ # if ("torrent" in row[0] and total_time > 30 and rate > 10 and (row[2] == '443' or row[2] == '80')) or ("torrent" not in row[0] and total_time > 30 and rate > 10 and int(row[2]) not in [22, 1781, 59886, 35968]): #for iscx_file_vpn
+ # if (total_time > 20) and (("POP" in row[0] and rate > 150) or (("IMAP" in row[0] and rate > 10 and row[7][0] == '8') or row[0] == 'FTP_filetransfer' and total_time > 20 and rate > 100) or ( rate > 10 and "SFTP" in row[0])): #for CICNTTor_file
+ # if total_time > 20 and rate > 5: #for CICNTTor_file_tor
+ # if ("skype" in row[0] and total_time > 20 and rate > 3 and row[1][:2] == "10") or ("ftps" in row[0] and total_time > 20 and rate > 300 and row[2] != '1781') or ("sftp" in row[0] and total_time > 20 and rate > 5 and (('A' in row[0] and row[4]=='22') or ('B' in row[0] and row[2]=='22'))):#for iscx_file_vpn
+ if (total_time > 50 and rate<5) and (("whats" in row[0] and ("185.60." in row[1] or "185.60." in row[3])) or ("hang" in row[0] and ("216.58." in row[1] or "216.58." in row[3])) or ("book" in row[0] and ("192.114." in row[1] or "192.114." in row[3]))): #my_chat
+ # if (row[1] in ["131.202.240.242","131.202.240.45"] and row[3] in ["131.202.240.242","131.202.240.45"]) or ("gmail" in row[0] and "131.202.240.87" in [row[1], row[3]] and total_time > 40 and row[5] == 'TCP' and rate < 0.3): #for scx_chat
+ # if ("skype" in row[0] and (row[1] in ["86.4.212.228", '157.56.52.13', '64.4.23.162'] or row[3] in ["86.4.212.228", '157.56.52.13', '64.4.23.162'])) or (total_time > 20 and rate < 2 and (("205.188." not in (row[1]+row[3]) and "hang" not in row[0]) or ("hang" in row[0] and "216.58" in (row[1]+row[3])))): #for iscx_chat_vpn
+ # if total_time > 20 and rate < 2: #for CICNTTor_chat
+ # if total_time > 20 and (row[2] in ['80', '443'] or row[4] in ['80', '443']): #for CICNTTor_browsing_tor
+ # if total_time > 20:
+ writer1.writerow(row)
+ counter1 += 1
+ print(session_tuple_key, total_time, rate)
+ # session_spectogram(ts, sizes, session_tuple_key[0])
+ # elif "facebook" in row[0] and rate > 50:# --> voip
+ else:
+ writer2.writerow(row)
+ counter2 += 1
+ # # #
+ # if "skype" in row[0] and (row[1] in ["86.4.212.228", '157.56.52.13', '64.4.23.162'] or row[3] in ["86.4.212.228", '157.56.52.13', '64.4.23.162']):# and row[2] == '443' and rate >10:
+ # print session_tuple_key, total_time, rate
+ # session_spectogram(ts, sizes, session_tuple_key[0])
+
+ print("Total sessions in " + OUTPUT1 + " : " + str(counter1))
+ print("Total sessions in " + OUTPUT2 + " : " + str(counter2))
+ output1.close()
+ output2.close()
+
+ print(rate_list)
+ plt.hist(rate_list)
+ plt.show()
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', type=str, default=INPUT, help='Path to csvs folder')
+
+ FLAGS = parser.parse_args()
+ file_list = get_csvs_list(FLAGS.input, FILTER_LIST)
+ print("Total number of files " + str(len(file_list)) + " in " + INPUT)
+ traffic_csv_reader(file_list)
diff --git a/_reference/overlap_multiclass_reg_non_bn.ipynb b/_reference/overlap_multiclass_reg_non_bn.ipynb
new file mode 100644
index 0000000..a6f9fe5
--- /dev/null
+++ b/_reference/overlap_multiclass_reg_non_bn.ipynb
@@ -0,0 +1,1659 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import random\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Flatten\n",
+ "from keras.layers import Conv2D, MaxPooling2D\n",
+ "from keras.utils import np_utils\n",
+ "from keras.layers.normalization import BatchNormalization\n",
+ "from keras.callbacks import TensorBoard,ModelCheckpoint\n",
+ "from keras import backend as K\n",
+ "from keras.metrics import top_k_categorical_accuracy\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensorflow channels_first\n"
+ ]
+ }
+ ],
+ "source": [
+ "# def set_keras_backend(backend):\n",
+ "\n",
+ "# if K.backend() != backend:\n",
+ "# os.environ['KERAS_BACKEND'] = backend\n",
+ "# reload(K)\n",
+ "# assert K.backend() == backend\n",
+ "\n",
+ "# set_keras_backend(\"theano\")\n",
+ "K.set_image_data_format('channels_first')\n",
+ "print(K.backend(), K.image_data_format())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 128 #128\n",
+ "samples_per_epoch = 10\n",
+ "num_classes = 5\n",
+ "epochs = 40\n",
+ "class_names = [\"voip\", \"video\", \"file transfer\", \"chat\", \"browsing\"]\n",
+ "\n",
+ "# input hist dimensions\n",
+ "height, width = 1500, 1500\n",
+ "input_shape = (1, height, width)\n",
+ "MODEL_NAME = \"overlap_multiclass_reg_non_bn\"\n",
+ "PATH_PREFIX = \"D:/TS/Internet Traffic Classification/datasets/overlap_multiclass_reg/overlap_multiclass_\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import Train and Validation Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(3350, 1, 1500, 1500) (3350,)\n",
+ "(314, 1, 1500, 1500) (314,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_train = np.load(PATH_PREFIX + \"reg_x_train.npy\")\n",
+ "y_train_true = np.load(PATH_PREFIX + \"reg_y_train.npy\")\n",
+ "x_val = np.load(PATH_PREFIX + \"reg_x_val.npy\")\n",
+ "y_val_true = np.load(PATH_PREFIX + \"reg_y_val.npy\")\n",
+ "\n",
+ "print(x_train.shape, y_train_true.shape)\n",
+ "print(x_val.shape, y_val_true.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Shuffle Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(3350, 1, 1500, 1500) (3350,)\n",
+ "[ 2. 4. 4. 3. 3. 1. 1. 3. 4. 1.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "def shuffle_data(x, y):\n",
+ " s = np.arange(x.shape[0])\n",
+ " np.random.shuffle(s)\n",
+ " x = x[s]\n",
+ " y = y[s]\n",
+ " print (x.shape, y.shape)\n",
+ " return x, y\n",
+ "\n",
+ "x_train, y_train_true = shuffle_data(x_train, y_train_true)\n",
+ "\n",
+ "print(y_train_true[0:10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### convert class vectors to binary class matrices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0. 0. 1. 0. 0.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 1. 0.]\n",
+ " [ 0. 0. 0. 1. 0.]\n",
+ " [ 0. 1. 0. 0. 0.]\n",
+ " [ 0. 1. 0. 0. 0.]\n",
+ " [ 0. 0. 0. 1. 0.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 1. 0. 0. 0.]]\n",
+ "[[ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]\n",
+ " [ 0. 0. 0. 0. 1.]]\n",
+ "(3350, 5) (314, 5)\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_train = np_utils.to_categorical(y_train_true, num_classes)\n",
+ "y_val = np_utils.to_categorical(y_val_true, num_classes)\n",
+ "print(y_train[0:10])\n",
+ "print (y_val[0:10])\n",
+ "print(y_train.shape, y_val.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define and Compile model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(None, 10, 300, 300)\n",
+ "(None, 10, 150, 150)\n",
+ "(None, 20, 30, 30)\n",
+ "(None, 20, 15, 15)\n",
+ "(None, 4500)\n",
+ "(None, 64)\n",
+ "(None, 64)\n",
+ "(None, 5)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ "\n",
+ "def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ "def f1_score(y_true, y_pred):\n",
+ " prec = precision(y_true, y_pred)\n",
+ " rec = recall(y_true, y_pred)\n",
+ " return 2*((prec*rec)/(prec+rec))\n",
+ "\n",
+ "def top_2_categorical_accuracy(y_true, y_pred):\n",
+ " return top_k_categorical_accuracy(y_true, y_pred, k=2) \n",
+ "\n",
+ "from keras.layers.core import Activation\n",
+ "\n",
+ "model = Sequential()\n",
+ "# model.add(BatchNormalization(input_shape=input_shape, axis=-1, momentum=0.99, epsilon=0.001)) ############################\n",
+ "model.add(Conv2D(10, kernel_size=(10, 10),strides=5,padding=\"same\", input_shape=input_shape))\n",
+ "convout1 = Activation('relu')\n",
+ "model.add(convout1)\n",
+ "print(model.output_shape)\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "print(model.output_shape)\n",
+ "model.add(Conv2D(20, (10, 10),strides=5,padding=\"same\")) #################################################\n",
+ "convout2 = Activation('relu')\n",
+ "model.add(convout2)\n",
+ "print(model.output_shape)\n",
+ "model.add(Dropout(0.25))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "print(model.output_shape)\n",
+ "model.add(Flatten())\n",
+ "print(model.output_shape)\n",
+ "model.add(Dense(64, activation='relu'))\n",
+ "print(model.output_shape)\n",
+ "model.add(Dropout(0.5))\n",
+ "print(model.output_shape)\n",
+ "model.add(Dense(num_classes, activation='softmax'))\n",
+ "print(model.output_shape)\n",
+ "\n",
+ "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', top_2_categorical_accuracy, f1_score, precision, recall])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define nice_imshow and make_moasic functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import pylab as pl\n",
+ "import matplotlib.cm as cm\n",
+ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+ "import numpy.ma as ma\n",
+ "\n",
+ "def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None, bar=True):\n",
+ " \"\"\"Wrapper around pl.imshow\"\"\"\n",
+ " if cmap is None:\n",
+ " cmap = cm.jet\n",
+ " if vmin is None:\n",
+ " vmin = data.min()\n",
+ " if vmax is None:\n",
+ " vmax = data.max()\n",
+ " divider = make_axes_locatable(ax)\n",
+ " im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap,origin='lower')\n",
+ " if bar:\n",
+ " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+ " pl.colorbar(im, cax=cax)\n",
+ "\n",
+ "def plotNNFilter2(data, nrows, ncols, layer_name, cmap=None, bar=True):\n",
+ " \"\"\"Wrapper around pl.subplot with color bar\"\"\"\n",
+ " if cmap is None:\n",
+ " cmap = \"gray\"\n",
+ " \n",
+ " fig, axes = pl.subplots(nrows, ncols,figsize=(5*ncols, 4*nrows))\n",
+ " for i, ax in enumerate(axes.flat):\n",
+ " im = ax.imshow(data[:,:,i], interpolation=\"nearest\", cmap=cmap)\n",
+ " ax.set_yticklabels([])\n",
+ " ax.set_xticklabels([])\n",
+ " ax.invert_yaxis()\n",
+ "\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.05)\n",
+ " if bar:\n",
+ " fig.colorbar(im, ax=axes.ravel().tolist())\n",
+ " \n",
+ " pl.savefig(MODEL_NAME + \"_plotNNFilter2_\" + layer_name, bbox_inches='tight', pad_inches=1)\n",
+ " pl.show()\n",
+ "\n",
+ "def plotNNFilter(data, nrows, ncols, layer_name, cmap=None, bar=True):\n",
+ " \"\"\"Wrapper around pl.subplot\"\"\"\n",
+ " if cmap is None:\n",
+ " cmap = \"gray\"\n",
+ " \n",
+ " pl.figure(figsize=(3*ncols, 3*nrows))\n",
+ " \n",
+ " for i in range(nrows*ncols):\n",
+ " pl.subplot(nrows, ncols, i+1)\n",
+ " pl.imshow(data[:,:,i], interpolation=\"nearest\", cmap=cmap)\n",
+ " pl.xticks([])\n",
+ " pl.yticks([])\n",
+ " pl.gca().invert_yaxis()\n",
+ " pl.subplots_adjust(wspace=0.025, hspace=0.05)\n",
+ " pl.savefig(MODEL_NAME + \"_plotNNFilter_\" + layer_name, bbox_inches='tight', pad_inches=1)\n",
+ " pl.show()\n",
+ " \n",
+ "def make_mosaic(imgs, nrows, ncols, border=1):\n",
+ " \"\"\"\n",
+ " Given a set of images with all the same shape, makes a\n",
+ " mosaic with nrows and ncols\n",
+ " \"\"\"\n",
+ " nimgs = imgs.shape[2]\n",
+ " imshape = imgs.shape[0:2]\n",
+ " \n",
+ " mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border,\n",
+ " ncols * imshape[1] + (ncols - 1) * border),\n",
+ " dtype=np.float32)\n",
+ " \n",
+ " paddedh = imshape[0] + border\n",
+ " paddedw = imshape[1] + border\n",
+ " for i in range(nimgs):\n",
+ " row = int(np.floor(i / ncols))\n",
+ " col = i % ncols\n",
+ " \n",
+ " mosaic[row * paddedh:row * paddedh + imshape[0],\n",
+ " col * paddedw:col * paddedw + imshape[1]] = imgs[:,:,i]\n",
+ " return mosaic\n",
+ "\n",
+ "def mosaic_imshow(imgs, nrows, ncols, cmap=None, border=1, layer_name=\"convout\"):\n",
+ " pl.figure(figsize=(3*ncols, 3*nrows))\n",
+ "# pl.suptitle('convout2')\n",
+ " nice_imshow(pl.gca(), make_mosaic(imgs, nrows, ncols, border=border), cmap=cmap)\n",
+ " pl.savefig(MODEL_NAME + \"_mosaic_imshow_\" + layer_name, bbox_inches='tight', pad_inches=1)\n",
+ " pl.show()\n",
+ "\n",
+ "# pl.imshow(make_mosaic(np.random.random((10, 10, 9)), 3, 3, border=1))\n",
+ "# pl.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " ..., \n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 5 4]]\n",
+ "1.0\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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8fJydac3r695t+27bzuW1zmf7+X4N9/rav3TMs/25zvT8vb5O+13vttrPn/Ncvs/b9N65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\n9VSSJEnS6Nl62ueKoiRJkiS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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convout1_before output shape : (10, 300, 300)\n",
+ "convout1_before output shape : (300, 300, 10)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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TgMiUNZA0s9+X9H5tdy09JukTkvZIkrv/tqR/Lul/MbOmpE1J97i7j9outVIA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\npSkxMd7t6DeZecxG7UNGmSufIidyn6V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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convout2_before output shape : (20, 30, 30)\n",
+ "convout2_before output shape : (30, 30, 20)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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wJgBWg04MXlChTZs3b65hTVa3KoKOOvX8xa/41VBhfaoNdmI/hs5w/nx66V92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\nsWNJbevWrUlNTfZWk2+ff/7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ExERSGxwcbHm5Xt5j/dixY0ntsssua3m5KqRO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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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N/wnHp6skYRPTwYkTJ2R93rx5QeuXtnfn/P1kNYahYKokYRN58E3EDw08UEEu\nlQ4VOp2pPiDt8326h00o1RY0cTpR7SltG8vjfY9eDQAAAAAiMJgCAAAAgAgMpgAAAAAgAoMpAAAA\nAIiQKICiUCh4J/SXk28C9cjIiKnV1dVlfThISf3q+tjYmFxWTVgONWPGDCaOoizSTrwPFRo04UN7\nP/WNjo6aWpp+0qdYLJp9pX3eq7CJ0hCr16lwhFMx3OB0kFX/WSwWzbbV+4VzhEwhX3wzBQAAAAAR\nGEwBAAAAQAQGUwAAAAAQgcEUAAAAAETIPk0iwuDgoKwzGfXUkcUE6rz4wjOGh4dNLW3AACrDN0m+\n3AEUeVL9akNDQwWOBEn4JtiXW6FQyKVfVkFSzjk3d+7czPeNfGTVZguFQi59cF9fn6yrNqpCVlBd\n1DvbxMSEXNYXgPdmaAUAAAAAEIHBFAAAAABEYDAFAAAAABEYTAEAAABABAZTAAAAABChKtP8fAlT\n/f39plZXV5f14SClQqFQ6UMoq9mzZyeqY/qJTfSpZtM5iXA6GxgYkPXQ9LpT7RlHP1ldhoaGZL2+\nvj56m9P9Gjc1Ncm6LwEO5dPb22tq8+fPT7XNPNoj30wBAAAAQAQGUwAAAAAQgcEUAAAAAERgMAUA\nAAAAESoeQKFCJXwTcxsbG7M+nMycPHnS1LKYED46OirrtbW1pnbs2DFTa2lpKfsxKb6JnKXnqVgs\nJtpu6fJ5hl+oz1RTU5Pb/qcD33VX7VZNgB4fHzc13zmeDsEnSe7X0Pal+hrnpv+k8DykDYsYGRkx\ntTwDJNJOkJ+cnJzy7xkzyv//rfSJ1SVN0IRzug9T/Zdz/r5puqDtZk+1x+PHj8tlm5ubg7Y5NjZm\nauV+HvLNFAAAAABEYDAFAAAAABEYTAEAAABABAZTAAAAABAhswCK0MnSKlQiSehA6YRZ57KZNJuE\nOn71K+Pz5s0r+759Ez8V3+T3Ut3d3bLe1tYWtP7+/ftNzfeL1qXnLklbKBaLpt2piYfO6bY4ODho\nakkCOdTkdRUyknbC73Tmm8Abek5eeeUVUzvvvPNSHZO6D2bO1F1jmgnIxWLRTMBW/Zdz4YE1u3fv\nNrUFCxbIbapADvXZGxoa5Pp5UZ9dheX4+pA0E4t9oUDqOqk+XRkeHpZ13/GXUqEWTz75pFz2uuuu\nC9qmT+mz09f/5hXuUulAjzR8z5407dMXMJKmX/K1Y/Uepc696it97wzlCHBIEzJ15MgRU1P9zZIl\nS5IfWBXfumJNAAAgAElEQVTJK/AsrdD7O8l7rZI0yCwG30wBAAAAQAQGUwAAAAAQgcEUAAAAAERg\nMAUAAAAAEVIHUPh+0VpNSgydbJZkQqGaJJk2lGJ8fNzUfBPSFTXxVE2gq/Tk3tDPlOSzKx0dHabm\nux6l7SnJuSgUCqk+U9pJjlkEimQhbfuupLRhE4pqi1n80n2hUDCTgH2TgkMnzC5fvjx4/6pfLPev\nwCfhC99Q50QFwWQRNJSkD1DPvtBQCR/17FDPyLRBEz6l18R3jdT9oZ7vqn0lubfyCptI0ieqe1M9\np7J4jie5HqF8n1NdO9U+1XK++yjtM9Y5e17VtXNOn6uFCxeamu8dVlH7Uu1BXY8k/VXaAAkVpqXW\n97XRvEKy0t7fKkBJtTHVRn1hLrHPFb6ZAgAAAIAIDKYAAAAAIAKDKQAAAACIwGAKAAAAACIwmAIA\nAACACIkivMbHx11PT8+UWmtrq1x2YGDA1BoaGkxteHjY1ObMmZPksIy0KU8q4USlo/iSsFQ9r4Q+\nX0KJSpdpa2szNZWOkiSh6vDhw0Hr+85daeJMkvNWLBZNolSSlKMsUu5U+pFK6lH3RhLquvnOndp/\nFml+qg+YO3duqm12d3ebmmrHSfafJCUpjcnJSdPf+fatrofqg5K0G9UvqjbS29trakn6gNCEJdX3\n+5bN6xoNDQ3Jukq3amxsNLWjR4+a2oIFC9IfWAlfqluaZ1+xWDR9oK+fzuLerqQk/V/oMyltm1Up\ncWm3qdq3L7lN7T80/VPdG86V5z2otO2XvpO+Tr2bqvejJOc0tJ309/ebmu+cKKHH5OtD1b6ySKgN\nTbb06evrM7Wmpqbg9UPbY5K0zdg2yjdTAAAAABCBwRQAAAAARGAwBQAAAAARGEwBAAAAQISCmkDm\nXbhQ6HbO7c7ucABjebFY1AkDJWifqADaJ6oZ7RPVjjaKahbUPhMNpgAAAAAAv8Gf+QEAAABABAZT\nAAAAABCBwRQAAAAARGAwBQAAAAARGEwBAAAAQAQGUwAAAAAQYWaShVtbW4srVqwIWnb//v2mNjk5\naWpLly4N3v+ePXtMbdasWabW0dERvM00jhw5IusLFy40tYMHD5pafX29XH/evHnpDkx4/vnnTe2i\niy5Ktc2xsTFTmz17tqn19PQELeecvZ4HDhxwvb29hZDjUe1TtRnnnFu2bFnIJqVXX31V1s8888zo\nbVbaCy+8YGoXXnhhbvsfHR01tdraWlNTbam1tTV4P5s2bTK1BQsWmFpoP7dr1y7X09MT1D5ra2uL\nDQ0NQdtdtWqVqe3YsSNouVPR8PCwqam+3znnZs5M9FgLou55db9v3brV1NauXSu3qX6WpFCwTWlk\nZMTU6urqgra5e/fu4PbZ3NxcbG9vn1Lr6+vzLWtq6n4dHx83tc2bN8ttqvt4+fLlpnb8+HFT6+7u\nlttU90dNTY2pqc+pzrtz+vn+2muvmdrKlSvl+mr/odQz1zl9f4Su73sWh76HqGfsokWL5LKqzf/q\nV7/qCf2dqZaWlmJnZ+eU2tGjR+WyTU1Nphba/+7atUvW1XPhwIEDptbY2Ghqc+fOlds8efKkqYW+\nR/mefeo519ZmT7G6j53T5y6UepY7l+557rse6j1uYGDA1NTn8d1Lpec+9Bmf6KmzYsUKt3HjxqBl\n/+Zv/sbU1If813/91+D9f+ITnzC10s7fOec++9nPBm8zDd+x33XXXab2uc99ztQuvvhiuf6NN96Y\n7sCEOXPmmFrotfTZt2+fqZV2dM45d++995qabzBTOhC+/fbbg49HtU/VZpxz7p577gnebqlrrrlG\n1h977LHobVaa6vzTto8ktm3bZmpr1qwxtX//9383tT/4gz8I3o96Wb3llltM7f777w/aXldXV/C+\nGxoa3LXXXjulNmOG/uOA//zP/zQ1dS+o5U5Fv/zlL01t8eLFclk1OE7r+uuvN7VHHnnE1N72treZ\n2k9/+lO5zdAX2y1btpjaunXr5DZLBwBvfetb5XJKe3u7ub9+8pOfyGXf/e53m5oa+PT29pra6tWr\n5Tbf9773mdpXvvIVU/vud79ral/96lflNv/jP/7D1NQg4Uc/+pGp+f7TTD3f3/Oe95jaAw88INdP\n86Lq+8/Bl19+2dTUoG3v3r2mpp7Zzjn3zne+09RUf/Xxj3/c1D796U/Lbao2f9ZZZwX/CG9nZ6f7\n/ve/P6Xm66vVPXvZZZcF7ecjH/mIrH/96183tb/92781tbe//e2mduWVV8ptqsGYupfuu+8+U/v9\n3/99uU31nLvttttM7V3vepdcX527UDt37pR19Z8L6t3wjjvuMDXf9VDvcU888YSpqbYcOmAOfcbz\nZ34AAAAAEIHBFAAAAABEKKi/YfXp6uoq5vmnP8iWuvahf7+bl66uLrdx48agv/lP2z5D5+34qL9b\nV3/WkMWcuCRC/7zI1zeoPyHIgu9vr0sluUbllmf7TEvNX8liflGo0L9ZrwYTExOmpv6MSvWfvrld\neajG9lnpfuV0oc6zmreeZg5XORQKhU3FYjHob6nStlH1+RXfn1+rfkBtM697Pu29pJ4JzmXzXFDn\nyXeeS1WyzwjtQ/lmCgAAAAAiMJgCAAAAgAgMpgAAAAAgAoMpAAAAAIjAYAoAAAAAIlQuyukUluRX\nxitJJaFU43HmJW0qnPphZFWrNJVIpNqs+pFt55xraWkp+zEplUzpOxVVMrlPSdLXVLpPDU08q2Ry\n33RBal8+1HmudHJfpYWmx/mo81fJc5r2XsozzS/NuZ8OfQbfTAEAAABABAZTAAAAABCBwRQAAAAA\nRGAwBQAAAAARqmtG8inidA5xQPULDcWor6/P+EgAS02Kpk9FtSgWi6Y2HSbI4/Q2OTlZ6UM4pfHN\nFAAAAABEYDAFAAAAABEYTAEAAABABAZTAAAAABAhdQDFyMiIrA8ODpraggUL0u4OSGR4eFjW6+rq\nTI1JxFOpc4Tyon1aM2eSi1QtfJPWJyYmTG3WrFlZH05VOF3uw+mCNhpmxgz73QnP+PLhmykAAAAA\niMBgCgAAAAAiMJgCAAAAgAgMpgAAAAAgQuqZvr4JbKquJlu/+uqrpvbKK6/Ibf72b/+2qZ08edLU\n+vr6TC1t+MXo6KipHT16VC6rPntLS0uq/Wdh9+7dpvbEE0+Y2rve9S65flNTk6k99thjptbR0WFq\n6ho559zs2bOn/HtoaEgup0xMTLjjx49PqY2Pj8tl58yZY2oHDx40tUWLFpmamsiZhPrs6lyeTkqv\nm3PONTc3m9pDDz1kau9973uD93PNNdeYmmqzPseOHZvyb1/7CqXaoY/aF2EN+VD9UH19fQWOJF9q\nEr9z6drd2NiYrJf2/ZWm3i2cq84Qg2KxaGoqKEO9g/nOe01NTdC+1TZ9/Zrv2qfhex6nfU5Xm9Br\nXK3U8atnWjXeXyFOrdYGAAAAADlhMAUAAAAAERhMAQAAAEAEBlMAAAAAEIHBFAAAAABESBTJMz4+\nbtKsfIkv8+bNM7Xa2lpTO//8803t2WefldtUKX9nnHGGqankPpUk4lx4Gkpvb6+p+dKcvvOd75ja\nHXfcYWp5phoNDg6a2ty5c03tyiuvNLUtW7bIbV5yySWmptLSlC9/+cuyfu211075d5JEnpqaGpMA\n50ujUlTq4JEjR0xt4cKFwdtUQlOS0tq5c6esr1y50tQmJydNLc80JJXcp7S1taXaT5LkPqU0lTNp\nqllpP9Td3S2Xmz9/vqllkXIUmhCo+g/nnGtoaAjaT5IkqtBkR5Ww6px+zqSVpk/2JZdOhwTPLBLt\nfOcyTVqlr59P09f63hmUEydOmJp6B8pK6HuMep4tX7481b7Vu5Evzc/Xj2RBtV3VntIm4qlU5yze\nQZMcp+pzVCKp717MIn262pIHVQqlc8kSdv8vvpkCAAAAgAgMpgAAAAAgAoMpAAAAAIjAYAoAAAAA\nIiSaPV0sFk1oQnt7e/D6amKxmuj2h3/4h3L9vXv3mlroxGDfZPrQSabqc/qCCNQkz61bt5ra2rVr\ng/ZdDp/+9KdN7Z577jG1kZERU1NBE2l9/OMfl/ULL7xwyr937NgRvM2JiQnTxkKDDZxz7k//9E9N\n7Ytf/GLw+qE2bdpkaldddVWqbf793/+9qf3t3/5t8Pp5hU2oSebOObd7925TW716tamtX78+1f6f\neuopUzvvvPNMTYWzlEPpJFzfpNzQCf5q4rsvFEJNvg6d4P+BD3xA1v/7v/87aP1du3aZ2qpVq+Sy\nqk/etm2bqa1ZsyZo30n4ngff+MY3TO2jH/2oqe3Zs8fUli1blv7ASvhCgR588MEp/z5w4EDwNgcH\nB90vfvGLKbVFixbJZVesWGFq3/3ud01NBUStW7dObjO0Lb722mtB+3EuPBhCBcH09/fLbfb09Jha\nFs/IJB5++GFTu/XWW00tbQCECjZQ52Px4sVy/dDAmnLIIrBHUWETSp4BDCrcRt1fvhC1NJKEFeV1\njRRf0ETpO2RoiBnfTAEAAABABAZTAAAAABCBwRQAAAAARGAwBQAAAAARCkl+5fucc84plk5wPf/8\n81MdwLFjx0wt7a8vq8m5vkmvWVBhE0uXLjU137nPYpKmCpaoq6szteeee87UNmzYELyf0oAS58JD\nQpSuri63cePGoJmbXV1dxY0bN06pffjDH5bLfuYznzG1M888M+iYHnroIVl/+umnTe3uu+82NTVh\nV63rnHO33HJL0DGlde+995raHXfckcu+ndOTVtV9cN9995maLyzitttuS39gJfbv3z/l3zfddJN7\n6aWXotunjwqx8QXeVFIWxzk0NGRqaqL06OioXL+2tjbV/qez0r7luuuucy+++GLZ22deQttCWqH9\nj3M6mKKmpsbU1PPVufzCfrKQxftaoVDYVCwWu0KWVW1UhWI4p8NX1Hvg9773PVNTISXO6bAJFZSk\nQgtefvlluc3Ozs6g2r59+0ztZz/7mdxmaZCX75jSBjopk5OTsq6eFeq+UaEUvuCq1tZWU1Pv1T//\n+c9N7ZlnnpHbfOc73znl37fffrv71a9+9aZ96PS9qwEAAACgghhMAQAAAEAEBlMAAAAAEIHBFAAA\nAABECPvJ8f/PnDlzTOBEb2+vXFZNLFOTyNSkuieeeEJu89JLLzW1trY2U+vo6JDr5+XkyZOmtnfv\nXlNbu3ZtHofjnPNPhi21a9cuU0sSQHH48GFTU+EbPqG/Nh3qgQceKPt+3vve9yaql1ITm3/rt34r\n+nh8fJM21STiPMMmFHVOVFDHjTfeaGrqfvN56qmnTO3yyy8PXn/JkiVT/p32F9zVtXDOuebm5uht\nnjhxQtZ9k6rTSBM24btuKmBATSqudNCE6kPUcy8J9TnVedq+fbtc/6yzzpry75kzEz3iU1HHXigE\nZV8455wbHh42tTlz5gSt6+vrQj9/klCIxsbG4GWnA18fpIIlQsMmVN/tnA4MSMt3PVQwhArIuuKK\nK0zN1/+q0BvV7lUfdtlll8lthlKhFL/zO78jl1V9hnpWqXvOufD7TvHdS+3t7UHrp+1XVZ+jrnFX\nl848KX1XDj0XfDMFAAAAABEYTAEAAABABAZTAAAAABCBwRQAAAAARGAwBQAAAAAREkX9TExMuOPH\nj0+pzZ8/P3h9lfS2aNGioNob1UtlkVql+NKoVq1aZWoq3WVyclKunyRZqNyuuuqqVOur5D6VduNL\n00uTPjU+Pm6SiXzpQyodRh3TD37wA1O78sor5TZDE9jSJOUk4TuXoQls6ro5lyyhKw2V/KSSPn3X\nQ1H33MMPP2xqt956a/A2Q42OjrodO3ZMqam+Igl1jVRyqHP59Yuhktzrqs1Vuv8MTZgaGhqSdfVM\nUJ9z9uzZplaa2lcN0vYLKm0sdJtpUwtVn6yS35zT16MaDQwMmJo69tCEPh+VyBz6rhajtM87evSo\nXC40OTDJ9VTL5vU8TCK0D0ybRpuFQ4cOmVppim45pE1eLcU3UwAAAAAQgcEUAAAAAERgMAUAAAAA\nERhMAQAAAECERLM2BwYG3FNPPTWldtNNNwWvv2DBAnsACSaOqom8ahJvXvbv3y/rK1asCFq/kkET\nPllMHFUTNNNOGFZmzpxpAlFGRkbksnV1dabW19dnarfccoupjY+PRx7h9FKNE2tV2ESSieJXXHFF\n2Y8plGqfaalrtHbtWrmsCquo5DVOsu/BwUFTa2hoKOfhJBZ6Piv5jEpiYmLC9IGqn3QufNK+Cgnx\nPfeyeCakkSSYQD0Tsvg8vuAmNZl+7ty5Zd+/0tnZmct+Xld6j6XtB9S18z3j1T2fV6BUFrJoo77g\nKkX1l4sXLzY13/VIc/zlDt+ovrd5AAAAAJgGGEwBAAAAQAQGUwAAAAAQgcEUAAAAAERINHtr3rx5\n7sYbb4ze2ZEjR0xNTTbzURN5Qye4njx5Um4zzSS00KCJPB07dkzWm5qaTE2dk+k8mXJ8fNz19PRM\nqbW1tQWvHxoO4Jv0qMIufBO4S6lfkXcu/8m9lTI8PGxqoW0xyUTxSqqpqSl7AIXi69PU5HU1sbe2\ntrbsx5SEOqa8wib6+/tlvbGx0dTU+ay2EIUkampqzOdMEhKinidJzkeSsIpQWYSuqOPM67r7zocK\n4Zku/WJaKnwjCXXtKt1u00p7TtJQ58M53a+rPlS950+HfpVvpgAAAAAgAoMpAAAAAIjAYAoAAAAA\nIjCYAgAAAIAIiWd1pZm8GRo2sWfPHllXk5BbWlqCtlnuXzt+IyoEQgURqIl2aYWeD+emx6S+JGbO\nnOkWLFgQvb4KkFATSQ8fPizXX7p0qamNjo6amprgv2jRopBDzMzBgwdNraOjI7f9T+fgk1AqIKW1\ntbXs+xkcHJR11X+G9ue+X7VPO5lfCe2X1ORl59JNvlZBEz6nWv9ZLBZNkEGSMJLQZ6yaiO6c7mvT\nBkjkFUChjj2L9uk79tMlbEJJ8tlDQ6KS9HeVDpsI1d3dbWq+YJ8076a+9l3JUIw8TI9WAAAAAABV\nhsEUAAAAAERgMAUAAAAAERhMAQAAAEAEBlMAAAAAECFRHNHY2Jjbt2/flFpnZ2dZD8g555YtW1b2\nbeZJJcFkkdw3nSVJHEuiNFln27Ztcrk1a9aYmkr1KU23ck6n9vmEpmFlkTZ55MgRWVfH1NbWZmoq\nidC3Pt7czJkzTXpfX1+fXLapqcnUVAqaap9JUuZCk6iySO1L61RPh3rd8PCwqfnuwTTJYoVCwSSj\nqeQ6337Uc0/VfNvMIh0xi6Q1dZwnT540tTwThCspi8TEJFQf6JxO+Qu9Jr5nn0oDbG5uDtpmpc2f\nP9/UfKmFSI5vpgAAAAAgAoMpAAAAAIjAYAoAAAAAIjCYAgAAAIAIiWZ8zp49u+yBE8ePHzc134S+\niYkJU1OTkLds2WJq69atizi6OAsWLDC1oaEhU/NNuFUTJ7PQ3d1taiqIIC0VhLBw4cKy72dyctIN\nDAxMqamgCZ+enh65zVK+MJG5c+cG7ysPSc7x5s2bTW39+vXlPJyySNJfKGoCsQpDUfdwWpOTk6Yf\nUEETPqq/SDJp/9ChQ6bW3t4evH61UcEMzjk3Z86csu9LtTt17dSE7iRBGaX9l3M6GCeLYAUlyX5U\n6IC635JcHxXsoM5xXs9M53Q4gQqSURP+ncsmaEM5ceKEqak2q96rnNPHGRo2kee9qdqIc7pNhN6L\nvoAXdS9OF729vaZW6fAM1d9V23tUKL6ZAgAAAIAIDKYAAAAAIAKDKQAAAACIwGAKAAAAACIkmgk5\nPj5uwgR8E7VDJ/qlnTyuftE6z8moynPPPWdqGzZsqMCR/P9UAMZ0DptQZsyYYSYvqkm4zjk3b948\nU+vv7ze1jo4OU/NNQj18+LCpLVq0yNT2799vakuWLJHbzEsWbSGJX//616Z21llnmZrqL5JMdlbX\nTgWPZGFiYsK0MV9byiJgQIVNqInzSUIx8nLs2DFTa2lpyW3/qt2F9nXqueWcvvahk69VaIpzzjU0\nNAStrxSLRRNG4AsnUBP0VViPase+PlmdD18QQMi+fftPS7VF9ZxQYQ15Us84FZ4Reo6d02EP6n0r\ni6AJnyze93zX7ujRo6am+qG8AmJ8VLCD6jNaW1vzOBznnD6neQV6hPZNafDNFAAAAABEYDAFAAAA\nABEYTAEAAABABAZTAAAAABCBwRQAAAAAREiU5uecTcAITe0rh/HxcVNT6UWrVq0yNZVC45xOAwx1\n6NAhWa90cp9SX18ftNyWLVtMbd26dcH7SZvcV5rM5ktpCqUSjXxUKk+hUAheXyX3KVu3bjW1tGl+\nKmXIl7SpqHsrTyq5b/v27aa2evVqU0ubHNXZ2WlqKuXOuXRJd7NmzQpuI4pKQ1JpazNnhnfroZ/n\nqaeekvXLL788eF+lVBqec7oPyTO5T1HnWR3npk2bTO3iiy8O3o+6xurZ5Uvte+mll6b825d06VPa\nD/iS0lR/oY5TvR+oRDnndLqWej6H7sc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Vcs7pCWP9/f2m5vtlY6V0wq1vP0mo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\ndXUl72fOnDlu+fLlyesDb6vVJPk5c+iC8c7Gx8dN7fz583LZ6667Lnk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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize the first layer of convolutions on an input image\n",
+ "i = 35\n",
+ "X = x_train[i][0]\n",
+ "print(X)\n",
+ "print(y_train_true[i])\n",
+ "pl.figure(figsize=(15, 15))\n",
+ "pl.title('input')\n",
+ "nice_imshow(pl.gca(), np.squeeze(X), vmin=0, vmax=1, cmap=cm.binary)\n",
+ "pl.savefig(MODEL_NAME + \"_input\", bbox_inches='tight', pad_inches=1)\n",
+ "pl.show()\n",
+ "\n",
+ "# Visualize convolution result (after activation)\n",
+ "def get_layer_output(layer, input_img, layer_name):\n",
+ " convout_f = K.function(model.inputs, [layer.output])\n",
+ " C = convout_f([input_img])\n",
+ " C = np.squeeze(C)\n",
+ " print(layer_name + \" output shape : \", C.shape)\n",
+ " C = np.transpose(C)\n",
+ " C = np.swapaxes(C,0,1)\n",
+ " print(layer_name + \" output shape : \", C.shape)\n",
+ " return C\n",
+ " \n",
+ "\n",
+ "C1 = get_layer_output(convout1, x_train[i:i+1], layer_name=\"convout1_before\")\n",
+ "mosaic_imshow(C1, 2, 5, cmap=cm.binary, border=2, layer_name=\"convout1_before\")\n",
+ "plotNNFilter(C1, 2, 5, cmap=cm.binary, layer_name=\"convout1_before\")\n",
+ "plotNNFilter2(C1, 2, 5, cmap=cm.binary, layer_name=\"convout1_before\")\n",
+ "\n",
+ "C2 = get_layer_output(convout2, x_train[i:i+1], layer_name=\"convout2_before\")\n",
+ "mosaic_imshow(C2, 4, 5, cmap=cm.binary, border=2, layer_name=\"convout2_before\")\n",
+ "plotNNFilter(C2, 4, 5, cmap=cm.binary, layer_name=\"convout2_before\")\n",
+ "plotNNFilter2(C2, 4, 5, cmap=cm.binary, layer_name=\"convout2_before\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "W1 shape : (10, 10, 10)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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WYlhm+9tmdkxlsxGVmRX2IUldt1k3SNIUd28laUpm+WvMrEDScEndJLWW1MfM\nWic6WgAAAADIc5XstbpJapW59ZM0YieyO63CxtLdp0n6eJvVPSWNztwfLemMMqLtJRW5+xJ33yRp\nbCYHAAAAAFWuqkclE4xYVqbX6inpYS8xXVJtM2tYyexOi77Hsr67F2fufyCpfhn7NJa0rNTycknH\nlfeAZtZPJZ20mjVrFjwsAAAAAKicHH6PZWV6rbL2aVzJ7E5LPHmPu7uZJf4wTHcvlFQolXyOZdLH\nAwAAAIAdyfLGsq6ZzS61XJjpmbJStLFcZWYN3b04M5y6uox9VkhqWmq5SWYdAAAAAFSpHJgVdo27\ntytnW2V6rfL2qVaJ7E6rzOQ9ZZkgqW/mfl9Jz5SxzyxJrcyshZlVl9Q7kwMAAACAKlfV76NM8B7L\nyvRaEyRdmJkdtoOkTzNvZ9wlfVqFI5Zm9mdJnVQyFLtc0hBJt0kaZ2aXSFoqqVdm30aS7nf37u6+\n2cyukDRJUoGkUe4+L+kBAwAAAEA+K6/XMrNLM9tHSpooqbukIkkbJF20o2zSY6qwsXT3PuVsOrmM\nfVeq5OC3Lk9UyTcEAAAAAFklyy+F3aGyeq1MQ7n1vkvqX9lsUokn7wEAAACAXJTLjWW2obEEAAAA\nkJdoLNOTlY3lunXr9NJLL4WyK1euDOUuvPDCUE6SOnXqFM6eccYZodyDDz4YrpnkeKdOnRrOnnnm\nmaFc27ZtwzV79ox/1utjjz0Wyt17773hmh07dgxnR4wYEc4edthhodyYMWPCNb///e+HsxdddFEo\n98QTT4RrLl68OJxdv359KHf99deHa86dOzec7dGjRzj78ccfh3JTpkwJ16xRo0Y4+8orr4Ryt912\nW7jmO++8E84WFsZmmd+wYUO4ZsuWLcPZJOfwpEmTQrkkvx+TPC+dc845odzw4cPDNZMc70033RTO\nXnnllaHc6NGjwzWfffbZcPbVV18N5e6+++5wzRNPPDGc7dOnvHeh7ViS13eXXHJJOJvkdVr05zR5\n8uRQrm7duseWXs6BWWFzSlY2lgAAAACwq9FYpif6cSMAAAAAAEhixBIAAABAnmLEMj00lgAAAADy\nEo1lemgsAQAAAOQlGsv00FgCAAAAyDvMCpsuGksAAAAAeYnGMj3MCgsAAAAASIQRSwAAAAB5iRHL\n9NBYAgAAAMhLNJbpobEEAAAAkJdoLNNDYwkAAAAg7zArbLpoLAEAAADkJRrL9DArLAAAAAAgkawc\nsdyyZYtBinTTAAAbi0lEQVQ++eSTUPZvf/tbKPfyyy+HcpJ0++23h7Pt2rUL5V5//fVwzVmzZoWz\nU6ZMCWfHjRsXyk2bNi1cs2fPnuFss2bNQrkk58ORRx4Zzr7//vvh7Nq1a0O5vn37hmtefvnl4exH\nH30Uyh166KHhmhdeeGE4G7XHHvG//SX5Xg8++OBwds2aNaHcBRdcEK559NFHh7PHHHNMKHfuueeG\na55xxhnh7DXXXBPORi1atCicfffdd8PZNm3ahHK//vWvwzUPOeSQcLZJkyahXJKfUbSmJK1fvz6c\nbdq0aTgbNX78+HD2kUceCeXq1q0brnnzzTeHs0OHDg3lkjwXNmrUKJx97rnnwtnf/OY3odxpp50W\nypX1GpgRy/RkZWMJAAAAALsajWV6aCwBAAAA5CUay/TQWAIAAADIO8wKmy4aSwAAAAB5icYyPcwK\nCwAAAABIhBFLAAAAAHmJEcv00FgCAAAAyEs0lumhsQQAAACQl2gs00NjCQAAACDvMCtsupi8BwAA\nAACQSLixNLNvm9mcUre1ZjZgm306mdmnpfa5PvkhAwAAAEByW0cts/GWa8KXwrr7IklHSZKZFUha\nIempMnb9h7v3iNYBAAAAgF0hFxu4bJXWeyxPlvSeuy9N6fEAAAAAYJeisUxPWo1lb0l/LmfbCWb2\ntkpGNH/p7vPK2snM+knqJ0kHHnigNmzYEDqQevXqhXIdO3YM5STpvvvuC2cvuOCCUK5t27bhml9+\n+WU4O2PGjHD2mmuuCeVef/31cM0kBg0aFMrddNNN4Zrr1q0LZ5977rlw9ve//30oV1xcHK45duzY\ncLZ27drhbNQee3zzb0l/5plnwtnOnTuHs0meD5988slQburUqeGaSbLjx48P5X74wx+Ga7777rvh\nbP369UO5adOmhWteffXV4WySF2yLFy8O5Xr37h2umeS5pUuXLqHcoYceGq75s5/9LJytU6dOOHvj\njTeGs1FjxowJZ9u1axfKXXvtteGaH3zwQTh7ww03hHKff/55uGaScymJ6Gum1157LZRbuHDhduto\nLNOTuLE0s+qSTpdUVtfwpqRm7r7ezLpLelpSq7Iex90LJRVK0iGHHOJJjwsAAAAAypOr72XMVmn8\nCb6bpDfdfdW2G9x9rbuvz9yfKKmamdVNoSYAAAAAoAxmdoCZTTazxZmvZV6mYGZdzWyRmRWZ2aBS\n6283s4Vm9raZPWVmFV7SkUZj2UflXAZrZg0s82cAM2ufqfdRCjUBAAAAIJGqnvl1F84KO0jSFHdv\nJWlKZnnb771A0nCVDBS2ltTHzFpnNk+W1Mbdj5T0rsq+OvVrEl0Ka2b7SOoi6Wel1l0qSe4+UtI5\nki4zs82SPpfU2925zBUAAABAlduNL4XtKalT5v5oSVMl/XqbfdpLKnL3JZJkZmMzufnu/kKp/aar\npK/boUSNpbt/JunAbdaNLHX/Hkn3JKkBAAAAALvCbtxY1nf3rbMsfiCprNnfGktaVmp5uaTjytjv\nYkmPV1QwrVlhAQAAACBn5MDkPXXNbHap5cLMhKeSJDN7UVKDMnJfm9LY3d3MQleNmtm1kjZLqnBq\nZhpLAAAAAHkpyxvLNe5e7ufluPsp5W0zs1Vm1tDdi82soaTVZey2QlLTUstNMuu2PsaPJfWQdHJl\n3s74zX8wGwAAAABgV5ogqW/mfl9JZX049ixJrcysReYjJHtncjKzrpJ+Jel0d99QmYKMWAIAAADI\nS1k+YpnEbZLGmdklkpZK6iVJZtZI0v3u3t3dN5vZFZImSSqQNMrd52Xy90jaS9LkzM9ourtfuqOC\nNJYAAAAA8tLu2li6+0eSTi5j/UpJ3UstT5Q0sYz9vrWzNWksAQAAAOSl3bWxrAo0lgAAAADyTg7M\nCptTaCwBAAAA5CUay/QwKywAAAAAIBGrxEeSfOMaNGjgffv2rXjHMhx44IGh3HvvvRfKSdLMmTPD\n2X/84x+h3KZNm8I1n3322XD2nHPOCWcXL14cyl122WXhmq+99lo4u9dee4Vy1atXD9dcunRpOPv8\n88+Hs8uXLw/lunfvXvFO5ahdu3Y4++6774ZyNWrUCNc86KCDwtlWrVqFcosWLQrXvP/++8PZ6Pkg\nSVdffXUo17x583DNjh07hrPjxo0L5R566KFwzejzviTNnj274p3KkOR3/fz588PZXr16hbPdunUL\n5U488cRwzSZNmoSzt99+eygX/T8jSS+99FI4+9RTT4WzZ555Zig3ePDgcM0kr3vat28fyiX5f5Pk\neIuLi0O5m266KVzztNNOC2cPPvjgcPbee+8N5c4+++xQrmvXrnrrrbf+M0TZsmVLv+2220KP9U3o\n1avXGzv6HMtsw6WwAAAAAPISl8Kmh8YSAAAAQF6isUwPjSUAAACAvMOssOmisQQAAACQl2gs08Os\nsAAAAACARBixBAAAAJCXGLFMD40lAAAAgLxEY5keGksAAAAAeYnGMj00lgAAAADyDrPCpovGEgAA\nAEBeorFMD7PCAgAAAAASYcQSAAAAQF5ixDI9NJYAAAAA8hKNZXpoLAEAAADkJRrL9GRlY/nhhx/q\n3nvvDWUbNWoUyiU5qf7617+GszVr1gzl1q1bF65Zq1atcHbMmDHh7DvvvBPKHXjggeGaSZx77rmh\n3Pz588M1Bw8eHM7ecsst4WzdunVDuZ/97Gfhmg8++GA4G7Vw4cJw9qc//WmKR1I5derUCWdPOumk\ncPawww4LZ8ePHx/Kff/73w/XrFevXji79957h3JJfr5Jzv2///3v4WzUnnvGXyr06NEjnL3mmmtC\nuXHjxoVrLl26NJy98sorQ7lDDjkkXLNfv37hbEFBQTg7cODAcDZqzpw54eySJUtCucceeyxcc+PG\njeHsxRdfHMo999xz4Zr77bdfODtx4sRwtn79+qHc9ddfH8qtXLnya8vMCpuurGwsAQAAAGBXo7FM\nD7PCAgAAAAASYcQSAAAAQF5ixDI9iRpLM3tf0jpJWyRtdvd222w3SXdJ6i5pg6Qfu/ubSWoCAAAA\nQBpoLNOTxohlZ3dfU862bpJaZW7HSRqR+QoAAAAAVYrGMj27+lLYnpIedneXNN3MaptZQ3cv3sV1\nAQAAAKBczAqbrqST97ikF83sDTMra87rxpKWlVpenlm3HTPrZ2azzWx2SR8KAAAAAMgFSUcsT3L3\nFWZWT9JkM1vo7tMiD+TuhZIKJamgoIDOEgAAAMAuxYhlehI1lu6+IvN1tZk9Jam9pNKN5QpJTUst\nN8msAwAAAIAqRWOZnvClsGa2j5nV2npf0qmS5m6z2wRJF1qJDpI+5f2VAAAAALLB1vdZZuMt1yQZ\nsawv6anMN72npMfc/Xkzu1SS3H2kpIkq+aiRIpV83MhFyQ4XAAAAANKRiw1ctgo3lu6+RFLbMtaP\nLHXfJfWP1gAAAACAXSFXRwazVdJZYQEAAAAAeW5Xf45lyN577602bdqEsiNHjqx4pzLceeedoZwk\nvfjii+FszZo1Q7mbbropXHPDhg3h7GeffRbOduvWLZT76quvwjWTaNt2uwH5Shk2bFi45hFHHBHO\nXn311eHsww8/HMoNGTIkXLN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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "W2 shape : (10, 10, 20)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py:861: RuntimeWarning: overflow encountered in true_divide\n",
+ " resdat /= (vmax - vmin)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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AKBoRwYaxgPgaRgAAAABAJm4YAQAAAACZOJIKAAAAoKhwJLVw2DAC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\nFc6+8sor4ezo0aPD2ajhw4eHs1dddVUot2XLlnD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+ ""
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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LFAAAQIJlCgAAIMEyBQAAkGCZAgAASLBMAQAAJFimAAAAEixTAAAACZYpAACA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\nGqXZs2fPTrcrVqxo+GyrVq2iW7du6Vn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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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oCPfsAwAAAKCj3LOvPt1a7Js3b1786Ec/Sh3gF7/4RWovIuJXv/pV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\nAIAaVFU1NyL2Xs3rH4uIA1penhIRU1bzuJ9FxM+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AAABJRU5ErkJg\ngg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize weights\n",
+ "W1 = model.layers[0].get_weights()[0]\n",
+ "W1 = np.squeeze(W1)\n",
+ "# W1 = np.asarray(W1)\n",
+ "print(\"W1 shape : \", W1.shape)\n",
+ "\n",
+ "mosaic_imshow(W1, 2, 5, cmap=cm.binary, border=1, layer_name=\"conv1_weights_before\")\n",
+ "plotNNFilter(W1, 2, 5, cmap=cm.binary, layer_name=\"conv1_weights_before\")\n",
+ "plotNNFilter2(W1, 2, 5, cmap=cm.binary, layer_name=\"conv1_weights_before\")\n",
+ "\n",
+ "# Visualize weights\n",
+ "W2 = model.layers[3].get_weights()[0][:,:,0,:]\n",
+ "W2 = np.asarray(W2)\n",
+ "print(\"W2 shape : \", W2.shape)\n",
+ "\n",
+ "mosaic_imshow(W2, 4, 5, cmap=cm.binary, border=1, layer_name=\"conv2_weights_before\")\n",
+ "plotNNFilter(W2, 4, 5, cmap=cm.binary, layer_name=\"conv2_weights_before\")\n",
+ "plotNNFilter2(W2, 4, 5, cmap=cm.binary, layer_name=\"conv2_weights_before\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fit model on training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:24: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(...] - ETA: 29s - loss: 1.2952 - acc: 0.4271 - top_2_categorical_accuracy: 0.6085 - f1_score: nan - precision: 0.8089 - recall: 0.1649Epoch 00000: val_loss improved from inf to 1.00429, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00000: val_acc improved from -inf to 0.56369, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 390s - loss: 1.2589 - acc: 0.4516 - top_2_categorical_accuracy: 0.6250 - f1_score: nan - precision: 0.8199 - recall: 0.1930 - val_loss: 1.0043 - val_acc: 0.5637 - val_top_2_categorical_accuracy: 0.6688 - val_f1_score: 0.4889 - val_precision: 0.9526 - val_recall: 0.4204\n",
+ "Epoch 2/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.8743 - acc: 0.6832 - top_2_categorical_accuracy: 0.7934 - f1_score: 0.6471 - precision: 0.9414 - recall: 0.4939Epoch 00001: val_loss improved from 1.00429 to 0.79898, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00001: val_acc improved from 0.56369 to 0.74522, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 393s - loss: 0.8776 - acc: 0.6875 - top_2_categorical_accuracy: 0.8031 - f1_score: 0.6491 - precision: 0.9391 - recall: 0.4969 - val_loss: 0.7990 - val_acc: 0.7452 - val_top_2_categorical_accuracy: 0.9076 - val_f1_score: 0.5226 - val_precision: 0.8875 - val_recall: 0.4650\n",
+ "Epoch 3/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.8276 - acc: 0.7413 - top_2_categorical_accuracy: 0.8620 - f1_score: 0.6879 - precision: 0.9476 - recall: 0.5417Epoch 00002: val_loss improved from 0.79898 to 0.79674, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00002: val_acc improved from 0.74522 to 0.74841, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 390s - loss: 0.8346 - acc: 0.7359 - top_2_categorical_accuracy: 0.8617 - f1_score: 0.6845 - precision: 0.9399 - recall: 0.5398 - val_loss: 0.7967 - val_acc: 0.7484 - val_top_2_categorical_accuracy: 0.9363 - val_f1_score: 0.6040 - val_precision: 0.9311 - val_recall: 0.5701\n",
+ "Epoch 4/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.7083 - acc: 0.7821 - top_2_categorical_accuracy: 0.9080 - f1_score: 0.7112 - precision: 0.9529 - recall: 0.5686Epoch 00003: val_loss improved from 0.79674 to 0.68171, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00003: val_acc improved from 0.74841 to 0.79618, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 389s - loss: 0.6988 - acc: 0.7844 - top_2_categorical_accuracy: 0.9055 - f1_score: 0.7145 - precision: 0.9551 - recall: 0.5719 - val_loss: 0.6817 - val_acc: 0.7962 - val_top_2_categorical_accuracy: 0.9554 - val_f1_score: 0.5677 - val_precision: 0.9335 - val_recall: 0.5096\n",
+ "Epoch 5/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.6435 - acc: 0.7908 - top_2_categorical_accuracy: 0.9089 - f1_score: 0.7140 - precision: 0.9705 - recall: 0.5660Epoch 00004: val_loss improved from 0.68171 to 0.57862, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00004: val_acc improved from 0.79618 to 0.81847, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 386s - loss: 0.6416 - acc: 0.7937 - top_2_categorical_accuracy: 0.9102 - f1_score: 0.7142 - precision: 0.9721 - recall: 0.5656 - val_loss: 0.5786 - val_acc: 0.8185 - val_top_2_categorical_accuracy: 0.9904 - val_f1_score: 0.6011 - val_precision: 0.9885 - val_recall: 0.5414\n",
+ "Epoch 6/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.5592 - acc: 0.7795 - top_2_categorical_accuracy: 0.9358 - f1_score: 0.7448 - precision: 0.9040 - recall: 0.6372Epoch 00005: val_loss improved from 0.57862 to 0.44967, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00005: val_acc did not improve\n",
+ "10/10 [==============================] - 388s - loss: 0.5474 - acc: 0.7820 - top_2_categorical_accuracy: 0.9391 - f1_score: 0.7502 - precision: 0.9046 - recall: 0.6445 - val_loss: 0.4497 - val_acc: 0.8153 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.6574 - val_precision: 0.9775 - val_recall: 0.6146\n",
+ "Epoch 7/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.4426 - acc: 0.8203 - top_2_categorical_accuracy: 0.9748 - f1_score: 0.7975 - precision: 0.8898 - recall: 0.7248Epoch 00006: val_loss improved from 0.44967 to 0.38214, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00006: val_acc improved from 0.81847 to 0.82166, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 395s - loss: 0.4377 - acc: 0.8180 - top_2_categorical_accuracy: 0.9750 - f1_score: 0.7983 - precision: 0.8847 - recall: 0.7297 - val_loss: 0.3821 - val_acc: 0.8217 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8210 - val_precision: 0.8235 - val_recall: 0.8185\n",
+ "Epoch 8/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.4695 - acc: 0.7856 - top_2_categorical_accuracy: 0.9583 - f1_score: 0.7655 - precision: 0.8503 - recall: 0.6979Epoch 00007: val_loss did not improve\n",
+ "Epoch 00007: val_acc did not improve\n",
+ "10/10 [==============================] - 378s - loss: 0.4623 - acc: 0.7914 - top_2_categorical_accuracy: 0.9617 - f1_score: 0.7686 - precision: 0.8545 - recall: 0.7000 - val_loss: 0.3907 - val_acc: 0.8217 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.6938 - val_precision: 0.9742 - val_recall: 0.6465\n",
+ "Epoch 9/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3893 - acc: 0.8316 - top_2_categorical_accuracy: 0.9757 - f1_score: 0.8126 - precision: 0.8807 - recall: 0.7561Epoch 00008: val_loss improved from 0.38214 to 0.33514, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00008: val_acc did not improve\n",
+ "10/10 [==============================] - 393s - loss: 0.3894 - acc: 0.8313 - top_2_categorical_accuracy: 0.9742 - f1_score: 0.8164 - precision: 0.8809 - recall: 0.7625 - val_loss: 0.3351 - val_acc: 0.8217 - val_top_2_categorical_accuracy: 0.9968 - val_f1_score: 0.8131 - val_precision: 0.8415 - val_recall: 0.7898\n",
+ "Epoch 10/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.4004 - acc: 0.8212 - top_2_categorical_accuracy: 0.9705 - f1_score: 0.8152 - precision: 0.8494 - recall: 0.7839Epoch 00009: val_loss did not improve\n",
+ "Epoch 00009: val_acc did not improve\n",
+ "10/10 [==============================] - 377s - loss: 0.3923 - acc: 0.8227 - top_2_categorical_accuracy: 0.9695 - f1_score: 0.8181 - precision: 0.8532 - recall: 0.7859 - val_loss: 0.3519 - val_acc: 0.8185 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8069 - val_precision: 0.8461 - val_recall: 0.7771\n",
+ "Epoch 11/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.3791 - acc: 0.8385 - top_2_categorical_accuracy: 0.9748 - f1_score: 0.8228 - precision: 0.8851 - recall: 0.7691Epoch 00010: val_loss did not improve\n",
+ "Epoch 00010: val_acc improved from 0.82166 to 0.83439, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 387s - loss: 0.3787 - acc: 0.8391 - top_2_categorical_accuracy: 0.9734 - f1_score: 0.8247 - precision: 0.8867 - recall: 0.7711 - val_loss: 0.3396 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.7925 - val_precision: 0.9301 - val_recall: 0.7325\n",
+ "Epoch 12/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.3155 - acc: 0.8594 - top_2_categorical_accuracy: 0.9835 - f1_score: 0.8521 - precision: 0.8805 - recall: 0.8255Epoch 00011: val_loss improved from 0.33514 to 0.31458, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00011: val_acc did not improve\n",
+ "10/10 [==============================] - 380s - loss: 0.3219 - acc: 0.8562 - top_2_categorical_accuracy: 0.9828 - f1_score: 0.8472 - precision: 0.8751 - recall: 0.8211 - val_loss: 0.3146 - val_acc: 0.8185 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8218 - val_precision: 0.8287 - val_recall: 0.8153\n",
+ "Epoch 13/40\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.3569 - acc: 0.8464 - top_2_categorical_accuracy: 0.9679 - f1_score: 0.8433 - precision: 0.8726 - recall: 0.8160Epoch 00012: val_loss did not improve\n",
+ "Epoch 00012: val_acc did not improve\n",
+ "10/10 [==============================] - 388s - loss: 0.3565 - acc: 0.8469 - top_2_categorical_accuracy: 0.9680 - f1_score: 0.8426 - precision: 0.8755 - recall: 0.8125 - val_loss: 0.3411 - val_acc: 0.8280 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8011 - val_precision: 0.8626 - val_recall: 0.7611\n",
+ "Epoch 14/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3400 - acc: 0.8663 - top_2_categorical_accuracy: 0.9774 - f1_score: 0.8487 - precision: 0.9114 - recall: 0.7943Epoch 00013: val_loss did not improve\n",
+ "Epoch 00013: val_acc did not improve\n",
+ "10/10 [==============================] - 390s - loss: 0.3349 - acc: 0.8688 - top_2_categorical_accuracy: 0.9773 - f1_score: 0.8521 - precision: 0.9118 - recall: 0.8000 - val_loss: 0.3253 - val_acc: 0.8248 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8120 - val_precision: 0.8298 - val_recall: 0.7962\n",
+ "Epoch 15/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3035 - acc: 0.8715 - top_2_categorical_accuracy: 0.9809 - f1_score: 0.8634 - precision: 0.8850 - recall: 0.8429Epoch 00014: val_loss did not improve\n",
+ "Epoch 00014: val_acc did not improve\n",
+ "10/10 [==============================] - 393s - loss: 0.3126 - acc: 0.8672 - top_2_categorical_accuracy: 0.9789 - f1_score: 0.8609 - precision: 0.8832 - recall: 0.8398 - val_loss: 0.3184 - val_acc: 0.8248 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8267 - val_precision: 0.8353 - val_recall: 0.8185\n",
+ "Epoch 16/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3455 - acc: 0.8611 - top_2_categorical_accuracy: 0.9766 - f1_score: 0.8489 - precision: 0.8912 - recall: 0.8108Epoch 00015: val_loss did not improve\n",
+ "Epoch 00015: val_acc did not improve\n",
+ "10/10 [==============================] - 397s - loss: 0.3391 - acc: 0.8664 - top_2_categorical_accuracy: 0.9766 - f1_score: 0.8547 - precision: 0.8962 - recall: 0.8172 - val_loss: 0.3251 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8044 - val_precision: 0.8827 - val_recall: 0.7580\n",
+ "Epoch 17/40\n",
+ " 9/10 [==========================>...] - ETA: 30s - loss: 0.2953 - acc: 0.8811 - top_2_categorical_accuracy: 0.9826 - f1_score: 0.8764 - precision: 0.9175 - recall: 0.8394Epoch 00016: val_loss did not improve\n",
+ "Epoch 00016: val_acc did not improve\n",
+ "10/10 [==============================] - 402s - loss: 0.2885 - acc: 0.8836 - top_2_categorical_accuracy: 0.9836 - f1_score: 0.8786 - precision: 0.9197 - recall: 0.8414 - val_loss: 0.3191 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8262 - val_precision: 0.8379 - val_recall: 0.8153\n",
+ "Epoch 18/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3459 - acc: 0.8628 - top_2_categorical_accuracy: 0.9757 - f1_score: 0.8612 - precision: 0.8824 - recall: 0.8411Epoch 00017: val_loss did not improve\n",
+ "Epoch 00017: val_acc did not improve\n",
+ "10/10 [==============================] - 391s - loss: 0.3416 - acc: 0.8617 - top_2_categorical_accuracy: 0.9750 - f1_score: 0.8609 - precision: 0.8832 - recall: 0.8398 - val_loss: 0.3164 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8248 - val_precision: 0.8387 - val_recall: 0.8121\n",
+ "Epoch 19/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.3054 - acc: 0.8802 - top_2_categorical_accuracy: 0.9809 - f1_score: 0.8737 - precision: 0.9129 - recall: 0.8377Epoch 00018: val_loss did not improve\n",
+ "Epoch 00018: val_acc improved from 0.83439 to 0.83758, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 385s - loss: 0.2979 - acc: 0.8828 - top_2_categorical_accuracy: 0.9828 - f1_score: 0.8763 - precision: 0.9135 - recall: 0.8422 - val_loss: 0.3300 - val_acc: 0.8376 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8125 - val_precision: 0.8360 - val_recall: 0.7930\n",
+ "Epoch 20/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2749 - acc: 0.8819 - top_2_categorical_accuracy: 0.9887 - f1_score: 0.8787 - precision: 0.9040 - recall: 0.8550Epoch 00019: val_loss did not improve\n",
+ "Epoch 00019: val_acc did not improve\n",
+ "10/10 [==============================] - 388s - loss: 0.2720 - acc: 0.8820 - top_2_categorical_accuracy: 0.9883 - f1_score: 0.8793 - precision: 0.9039 - recall: 0.8562 - val_loss: 0.3200 - val_acc: 0.8280 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8257 - val_precision: 0.8332 - val_recall: 0.8185\n",
+ "Epoch 21/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2833 - acc: 0.8845 - top_2_categorical_accuracy: 0.9774 - f1_score: 0.8819 - precision: 0.8983 - recall: 0.8663Epoch 00020: val_loss improved from 0.31458 to 0.30989, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00020: val_acc improved from 0.83758 to 0.84076, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 385s - loss: 0.2842 - acc: 0.8836 - top_2_categorical_accuracy: 0.9797 - f1_score: 0.8803 - precision: 0.8964 - recall: 0.8648 - val_loss: 0.3099 - val_acc: 0.8408 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8262 - val_precision: 0.8421 - val_recall: 0.8121\n",
+ "Epoch 22/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2672 - acc: 0.9010 - top_2_categorical_accuracy: 0.9878 - f1_score: 0.9000 - precision: 0.9227 - recall: 0.8785Epoch 00021: val_loss did not improve\n",
+ "Epoch 00021: val_acc did not improve\n",
+ "10/10 [==============================] - 384s - loss: 0.2695 - acc: 0.9016 - top_2_categorical_accuracy: 0.9883 - f1_score: 0.9005 - precision: 0.9217 - recall: 0.8805 - val_loss: 0.3175 - val_acc: 0.8408 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8312 - val_precision: 0.8416 - val_recall: 0.8217\n",
+ "Epoch 23/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.2544 - acc: 0.8958 - top_2_categorical_accuracy: 0.9861 - f1_score: 0.8916 - precision: 0.9127 - recall: 0.8715Epoch 00022: val_loss improved from 0.30989 to 0.30836, saving model to overlap_multiclass_reg_non_bn_loss.hdf5\n",
+ "Epoch 00022: val_acc improved from 0.84076 to 0.84395, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 384s - loss: 0.2631 - acc: 0.8922 - top_2_categorical_accuracy: 0.9859 - f1_score: 0.8886 - precision: 0.9086 - recall: 0.8695 - val_loss: 0.3084 - val_acc: 0.8439 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8361 - val_precision: 0.8412 - val_recall: 0.8312\n",
+ "Epoch 24/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.2546 - acc: 0.9010 - top_2_categorical_accuracy: 0.9852 - f1_score: 0.9007 - precision: 0.9165 - recall: 0.8854Epoch 00023: val_loss did not improve\n",
+ "Epoch 00023: val_acc did not improve\n",
+ "10/10 [==============================] - 379s - loss: 0.2471 - acc: 0.9055 - top_2_categorical_accuracy: 0.9859 - f1_score: 0.9050 - precision: 0.9215 - recall: 0.8891 - val_loss: 0.3085 - val_acc: 0.8439 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8322 - val_precision: 0.8438 - val_recall: 0.8217\n",
+ "Epoch 25/40\n",
+ " 9/10 [==========================>...] - ETA: 30s - loss: 0.2312 - acc: 0.9123 - top_2_categorical_accuracy: 0.9913 - f1_score: 0.9101 - precision: 0.9288 - recall: 0.8924Epoch 00024: val_loss did not improve\n",
+ "Epoch 00024: val_acc did not improve\n",
+ "10/10 [==============================] - 396s - loss: 0.2297 - acc: 0.9102 - top_2_categorical_accuracy: 0.9914 - f1_score: 0.9084 - precision: 0.9263 - recall: 0.8914 - val_loss: 0.3230 - val_acc: 0.8312 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8351 - val_precision: 0.8391 - val_recall: 0.8312\n",
+ "Epoch 26/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2455 - acc: 0.9028 - top_2_categorical_accuracy: 0.9852 - f1_score: 0.9037 - precision: 0.9209 - recall: 0.8872Epoch 00025: val_loss did not improve\n",
+ "Epoch 00025: val_acc did not improve\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 387s - loss: 0.2395 - acc: 0.9039 - top_2_categorical_accuracy: 0.9867 - f1_score: 0.9058 - precision: 0.9224 - recall: 0.8898 - val_loss: 0.3220 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8328 - val_precision: 0.8379 - val_recall: 0.8280\n",
+ "Epoch 27/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2223 - acc: 0.9132 - top_2_categorical_accuracy: 0.9913 - f1_score: 0.9088 - precision: 0.9261 - recall: 0.8924Epoch 00026: val_loss did not improve\n",
+ "Epoch 00026: val_acc did not improve\n",
+ "10/10 [==============================] - 393s - loss: 0.2205 - acc: 0.9117 - top_2_categorical_accuracy: 0.9914 - f1_score: 0.9071 - precision: 0.9252 - recall: 0.8898 - val_loss: 0.3141 - val_acc: 0.8408 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8335 - val_precision: 0.8429 - val_recall: 0.8248\n",
+ "Epoch 28/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2186 - acc: 0.9080 - top_2_categorical_accuracy: 0.9913 - f1_score: 0.9090 - precision: 0.9191 - recall: 0.8993Epoch 00027: val_loss did not improve\n",
+ "Epoch 00027: val_acc did not improve\n",
+ "10/10 [==============================] - 392s - loss: 0.2203 - acc: 0.9023 - top_2_categorical_accuracy: 0.9914 - f1_score: 0.9046 - precision: 0.9151 - recall: 0.8945 - val_loss: 0.3196 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8361 - val_precision: 0.8412 - val_recall: 0.8312\n",
+ "Epoch 29/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2261 - acc: 0.9141 - top_2_categorical_accuracy: 0.9878 - f1_score: 0.9097 - precision: 0.9186 - recall: 0.9010Epoch 00028: val_loss did not improve\n",
+ "Epoch 00028: val_acc did not improve\n",
+ "10/10 [==============================] - 397s - loss: 0.2350 - acc: 0.9133 - top_2_categorical_accuracy: 0.9891 - f1_score: 0.9089 - precision: 0.9179 - recall: 0.9000 - val_loss: 0.3173 - val_acc: 0.8408 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8391 - val_precision: 0.8476 - val_recall: 0.8312\n",
+ "Epoch 30/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2148 - acc: 0.9167 - top_2_categorical_accuracy: 0.9913 - f1_score: 0.9175 - precision: 0.9274 - recall: 0.9080Epoch 00029: val_loss did not improve\n",
+ "Epoch 00029: val_acc did not improve\n",
+ "10/10 [==============================] - 390s - loss: 0.2114 - acc: 0.9172 - top_2_categorical_accuracy: 0.9914 - f1_score: 0.9179 - precision: 0.9268 - recall: 0.9094 - val_loss: 0.3139 - val_acc: 0.8439 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8381 - val_precision: 0.8454 - val_recall: 0.8312\n",
+ "Epoch 31/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2210 - acc: 0.9149 - top_2_categorical_accuracy: 0.9905 - f1_score: 0.9170 - precision: 0.9289 - recall: 0.9054Epoch 00030: val_loss did not improve\n",
+ "Epoch 00030: val_acc did not improve\n",
+ "10/10 [==============================] - 382s - loss: 0.2145 - acc: 0.9164 - top_2_categorical_accuracy: 0.9906 - f1_score: 0.9190 - precision: 0.9305 - recall: 0.9078 - val_loss: 0.3309 - val_acc: 0.8376 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8361 - val_precision: 0.8412 - val_recall: 0.8312\n",
+ "Epoch 32/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.2235 - acc: 0.9210 - top_2_categorical_accuracy: 0.9939 - f1_score: 0.9199 - precision: 0.9377 - recall: 0.9028Epoch 00031: val_loss did not improve\n",
+ "Epoch 00031: val_acc improved from 0.84395 to 0.85032, saving model to overlap_multiclass_reg_non_bn_acc.hdf5\n",
+ "10/10 [==============================] - 378s - loss: 0.2180 - acc: 0.9195 - top_2_categorical_accuracy: 0.9945 - f1_score: 0.9184 - precision: 0.9352 - recall: 0.9023 - val_loss: 0.3188 - val_acc: 0.8503 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8434 - val_precision: 0.8532 - val_recall: 0.8344\n",
+ "Epoch 33/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.1781 - acc: 0.9193 - top_2_categorical_accuracy: 0.9965 - f1_score: 0.9215 - precision: 0.9309 - recall: 0.9123Epoch 00032: val_loss did not improve\n",
+ "Epoch 00032: val_acc did not improve\n",
+ "10/10 [==============================] - 389s - loss: 0.1766 - acc: 0.9203 - top_2_categorical_accuracy: 0.9969 - f1_score: 0.9223 - precision: 0.9315 - recall: 0.9133 - val_loss: 0.3491 - val_acc: 0.8344 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8309 - val_precision: 0.8338 - val_recall: 0.8280\n",
+ "Epoch 34/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.1805 - acc: 0.9332 - top_2_categorical_accuracy: 0.9887 - f1_score: 0.9346 - precision: 0.9459 - recall: 0.9236Epoch 00033: val_loss did not improve\n",
+ "Epoch 00033: val_acc did not improve\n",
+ "10/10 [==============================] - 384s - loss: 0.1829 - acc: 0.9320 - top_2_categorical_accuracy: 0.9898 - f1_score: 0.9336 - precision: 0.9456 - recall: 0.9219 - val_loss: 0.3223 - val_acc: 0.8503 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8457 - val_precision: 0.8544 - val_recall: 0.8376\n",
+ "Epoch 35/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.1979 - acc: 0.9332 - top_2_categorical_accuracy: 0.9957 - f1_score: 0.9292 - precision: 0.9413 - recall: 0.9175Epoch 00034: val_loss did not improve\n",
+ "Epoch 00034: val_acc did not improve\n",
+ "10/10 [==============================] - 386s - loss: 0.1970 - acc: 0.9313 - top_2_categorical_accuracy: 0.9961 - f1_score: 0.9273 - precision: 0.9385 - recall: 0.9164 - val_loss: 0.3261 - val_acc: 0.8471 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8391 - val_precision: 0.8476 - val_recall: 0.8312\n",
+ "Epoch 36/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.1753 - acc: 0.9314 - top_2_categorical_accuracy: 0.9922 - f1_score: 0.9260 - precision: 0.9384 - recall: 0.9141Epoch 00035: val_loss did not improve\n",
+ "Epoch 00035: val_acc did not improve\n",
+ "10/10 [==============================] - 384s - loss: 0.1817 - acc: 0.9336 - top_2_categorical_accuracy: 0.9922 - f1_score: 0.9279 - precision: 0.9398 - recall: 0.9164 - val_loss: 0.3396 - val_acc: 0.8376 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8396 - val_precision: 0.8416 - val_recall: 0.8376\n",
+ "Epoch 37/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.2160 - acc: 0.9314 - top_2_categorical_accuracy: 0.9948 - f1_score: 0.9301 - precision: 0.9366 - recall: 0.9236Epoch 00036: val_loss did not improve\n",
+ "Epoch 00036: val_acc did not improve\n",
+ "10/10 [==============================] - 374s - loss: 0.2121 - acc: 0.9336 - top_2_categorical_accuracy: 0.9938 - f1_score: 0.9327 - precision: 0.9405 - recall: 0.9250 - val_loss: 0.3224 - val_acc: 0.8503 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8469 - val_precision: 0.8533 - val_recall: 0.8408\n",
+ "Epoch 38/40\n",
+ " 9/10 [==========================>...] - ETA: 28s - loss: 0.1701 - acc: 0.9288 - top_2_categorical_accuracy: 0.9965 - f1_score: 0.9311 - precision: 0.9360 - recall: 0.9262Epoch 00037: val_loss did not improve\n",
+ "Epoch 00037: val_acc did not improve\n",
+ "10/10 [==============================] - 386s - loss: 0.1675 - acc: 0.9313 - top_2_categorical_accuracy: 0.9969 - f1_score: 0.9328 - precision: 0.9384 - recall: 0.9273 - val_loss: 0.3369 - val_acc: 0.8503 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8423 - val_precision: 0.8507 - val_recall: 0.8344\n",
+ "Epoch 39/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.2067 - acc: 0.9201 - top_2_categorical_accuracy: 0.9957 - f1_score: 0.9219 - precision: 0.9263 - recall: 0.9175Epoch 00038: val_loss did not improve\n",
+ "Epoch 00038: val_acc did not improve\n",
+ "10/10 [==============================] - 390s - loss: 0.2139 - acc: 0.9219 - top_2_categorical_accuracy: 0.9953 - f1_score: 0.9238 - precision: 0.9282 - recall: 0.9195 - val_loss: 0.3418 - val_acc: 0.8408 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8406 - val_precision: 0.8436 - val_recall: 0.8376\n",
+ "Epoch 40/40\n",
+ " 9/10 [==========================>...] - ETA: 29s - loss: 0.1778 - acc: 0.9358 - top_2_categorical_accuracy: 0.9957 - f1_score: 0.9368 - precision: 0.9405 - recall: 0.9332Epoch 00039: val_loss did not improve\n",
+ "Epoch 00039: val_acc did not improve\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 394s - loss: 0.1728 - acc: 0.9367 - top_2_categorical_accuracy: 0.9961 - f1_score: 0.9376 - precision: 0.9417 - recall: 0.9336 - val_loss: 0.3367 - val_acc: 0.8471 - val_top_2_categorical_accuracy: 0.9936 - val_f1_score: 0.8460 - val_precision: 0.8481 - val_recall: 0.8439\n"
+ ]
+ }
+ ],
+ "source": [
+ "tensorboard = TensorBoard(log_dir='./Graph', histogram_freq=1, write_grads=True, write_graph=True,\n",
+ " write_images=True, batch_size=batch_size)\n",
+ "checkpointer_loss = ModelCheckpoint(filepath= MODEL_NAME + '_loss.hdf5', verbose=1, save_best_only=True, save_weights_only=True)\n",
+ "checkpointer_acc = ModelCheckpoint(monitor='val_acc', filepath= MODEL_NAME + '_acc.hdf5', verbose=1, save_best_only=True, save_weights_only=True)\n",
+ "tensorboard.set_model(model)\n",
+ "\n",
+ "def generator(features, labels, batch_size):\n",
+ " index = 0\n",
+ " while True:\n",
+ " index += batch_size\n",
+ " if index >= len(features):\n",
+ " batch_features = np.append(features[index-batch_size:len(features)], features[0:index-len(features)], axis=0)\n",
+ " batch_labels = np.append(labels[index-batch_size:len(features)], labels[0:index-len(features)], axis=0)\n",
+ " index -= len(features)\n",
+ " yield batch_features, batch_labels\n",
+ " else:\n",
+ " yield features[index-batch_size:index], labels[index-batch_size:index]\n",
+ "\n",
+ "history = model.fit_generator(generator(x_train, y_train, batch_size),\n",
+ " epochs=epochs,\n",
+ " samples_per_epoch=samples_per_epoch,\n",
+ " verbose=1,\n",
+ " callbacks=[tensorboard,checkpointer_loss,checkpointer_acc],\n",
+ " validation_data=(x_val, y_val))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot history accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dict_keys(['val_loss', 'val_acc', 'val_top_2_categorical_accuracy', 'val_f1_score', 'val_precision', 'val_recall', 'loss', 'acc', 'top_2_categorical_accuracy', 'f1_score', 'precision', 'recall'])\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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jrXonL/V6kPiqfLjgAZh0ddMDqsJnj8K//wfSxzmNeH2aDdSp2A+fPgRLn3S6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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pickle\n",
+ "\n",
+ "with open(MODEL_NAME + \"_accuracy.pkl\", 'wb') as output:\n",
+ " pickle.dump(history.history, output, pickle.HIGHEST_PROTOCOL)\n",
+ "\n",
+ "# list all data in history\n",
+ "print(history.history.keys())\n",
+ "x = np.asarray(range(1,epochs + 1))\n",
+ "# summarize history for accuracy\n",
+ "plt.figure()\n",
+ "plt.plot(x, history.history['acc'])\n",
+ "plt.plot(x, history.history['val_acc'])\n",
+ "plt.title('model accuracy')\n",
+ "plt.ylabel('accuracy')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.legend(['train', 'val'], loc='upper left')\n",
+ "plt.savefig(MODEL_NAME + \" accuracy history\", bbox_inches='tight', pad_inches=1)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot Confusion Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "314/314 [==============================] - 29s \n"
+ ]
+ }
+ ],
+ "source": [
+ "y_val_prediction = model.predict_classes(x_val, verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[4 4 4 4 4 4 4 4 4 4]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y_val_prediction[:10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Confusion matrix, without normalization\n",
+ "[[59 0 2 0 0]\n",
+ " [ 0 69 0 0 1]\n",
+ " [ 0 0 63 0 0]\n",
+ " [ 0 0 0 14 40]\n",
+ " [ 0 0 0 5 61]]\n",
+ "Normalized confusion matrix\n",
+ "[[ 0.97 0. 0.03 0. 0. ]\n",
+ " [ 0. 0.99 0. 0. 0.01]\n",
+ " [ 0. 0. 1. 0. 0. ]\n",
+ " [ 0. 0. 0. 0.26 0.74]\n",
+ " [ 0. 0. 0. 0.08 0.92]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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oKPsjB7MITXavxeX7CIn1C0kdw6nUEfgy24E8kTrnGpwcq/bViheVZ0rqGVcNAt4FHgbK\nO1sPBbIOf+VVe+dcg5Nj1T4XZwF3SWoGfAKcQChg3ivpJOBTwh2Q1fJE6pxrWESuVfeszGwKUFk7\n6qCaHMcTqXOuQRG5Vd3rkydS51yD44nUOefyVFSUruvknkidcw2L4iNFPJE65xocr9o751wehLxq\n75xzeUtXgdQTaV3pu8l6jE/pSPTtBvw86RCqtGDiyKRDaLDKfmgkY43Lq/bOOZc3T6TOOZcHbyN1\nzrlCSFeB1BOpc66B8TZS55zLn1ftnXMuX+kqkHoidc41PF61d865PEh+1d455/LmJVLnnMtXuvKo\nJ1LnXAMjv2rvnHN5CfPaF+hY0gxgMVAGrDCz/pLWBUYBXYEZwOFmtqC646QrrbvVPPnE42zVuye9\ne3XnyisuTzoc2rRqzn+vPIkpD1zMG/dfzPZbbcKWm5Xw3G2/ZOK9v+W+a06ldcu1kw4zdZ9bpjTH\ndvrwE+nauQMDttky6VCqkX0q5hq2oe5uZn3NrHwSvAuBp82sB/B0XK6WJ9IUKysr45yzz+ShMY/x\nxlvvMvqeu3nv3XcTjemqCw7lyZffpe8hf2K7I/7C+598zg2/P5qLr32IAYdfxsPPvsm5Q2s0AWPB\npfFzK5fm2ACOOW4Y/xvzWNJhZFVUpKyPPBwE3Baf3wYcnDWefM7m6tbECRPo1q07m2y6Kc2aNeOw\nI45k7JiHEotnnVZrs8u23bj1wVcAWL6ijEXfLKX7Rhvw0qSPAHjm1fc5eFDfxGKE9H1umdIcG8Au\nu/6Edu3WTTqM6ilU7bM9gPaSXs94DK/kaAY8JWlSxvYOZjYnPv8c6JAtJG8jTbHZs0vp3LnLyuWS\nks5MmPBaYvF07bQe8xZ8w02XHMuWm5XwxnszOf+K+3jvkzkcMHArxjz3FofstS2dO7RLLEZI3+eW\nKc2xNRSCXEuc8zKq61XZxcxKJW0AjJP0fuZGMzNJWQd6bRQlUkmdJN1XxbbnJGX7sB1QXNyEvr26\ncPPoF9nxqL+yZOn3nH/iXpw64i6GH74r4++6gFYt1mLZ8rKkQ3VruBxLpFmZWWn8/5fAg8B2wBeS\nOobzqCPwZbbjNIpEamazzezQpOOoqU6dSpg1a+bK5dLSWZSUlCQWT+kXCyj9ciET3/4UgAefmkLf\nXl34cMYXHHDG9ex8zBXc+/gkps+am1iMkL7PLVOaY2swVJg2UkktJbUufw4MBt4GHgbKp7cYCmRt\ne1njEqmkyyWdmbE8QtL5kt6Oy80l3SPpPUkPAs0z9h0s6RVJkyWNltQqrh8k6Q1JUyXdImmt+ngv\n/QcM4KOPpjFj+nSWLVvG6FH3sP+QA+vj1JX6Yv5iZn2+gB4bbwDAwO168v4nn7N+u1ZAuNvkwlP2\n5ub7XkosRkjf55YpzbE1FKH7U0Gu2ncAXpL0JjABeMTMHgcuB/aSNA3YMy5Xa01sIx0FXANcH5cP\nB04FhsXl04ElZra5pK2AyQCS2gMXA3ua2beSfg2cJ+kK4FZgkJl9KOn2eIxrKp44NlYPB+iy0UZ5\nv5Hi4mL+/o+RHLD/3pSVlTF02Ils0bt33sfNx3l/Hc1/LhtGs+ImzCidx/A/3MkxQ7bn1CN+AsBD\nz0zh9odeTTTGNH5u5dIcG8Cw447mxReeY/68eWy2aRcu+t0Ihp5wUtJhVVDj7k2VMrNPgK0rWT8f\nqFHXE5mteRNmSXqP8EGsD/wTOAYYa2Z9JP0PuNbMnon7TiYkvw0JCXNWPEwz4BXgWuA6M/tJ3H8Q\ncKaZHVJdDP369bfxr71e6LdWED753ZopzZPftVqraFIOF35y0qJTT+t56g1Z95syYlDBzpnNmlgi\nBRgNHEpIjqNyfI2AcWZ21CorpdV+sZxzCarBxaT6ssa1kUajgCMJyXR0hW0vAEcDSOoDbBXXvwrs\nLKl73NZS0mbAB0DX8vXAccDzdRu+c64qBWwjLZg1MpGa2TtAa6A0o2NtuRuAVrH6fykwKb5mLqEd\n9W5JbxGq9b3M7DvgBGC0pKnAD8CN9fJGnHOVquM7m2psTa3aY2ZbZjyfAfSJz5cSSquVveYZYEAl\n658GtqmTQJ1zNZa2qv0am0idc2son0XUOefyI+q/6p6NJ1LnXIOTsgKpJ1LnXMPjVXvnnMtHCvuR\neiJ1zjUoYRi9dPXc9ETqnGtwvETqnHN58jZS55zLg+Tdn5xzLm8pK5BWnUglrVPdC83s68KH45xz\n2RWlLJNWVyJ9hzDDXmbE5csG5D9ysXPO1ZCU8+R39abKRGpmXara5pxzSUpZHs2tjVTSkcCmZnaZ\npM6EeZ8n1W1orq6keRT6dvv/LekQqrXgkV8mHUKVmqQtu9ShtF21z9qrVdJIYHfCgMYAS/DxOJ1z\nCRGhjTTbI+fjSU3i5JZj4/K6ksZJmhb/3y7bMXK5PWAnMzsV+A7AzL4izGfknHOJKFL2Rw38Angv\nY/lC4Gkz6wE8HZerjyeHkyyXVES4wISk9QijxDvnXP3LYZqRXKv+salyf+BfGasPAm6Lz28DDs52\nnFzaSK8H7gfWl3QJYXrjS3KK0jnnCkzk3B7cXlLmVL43mdlNFfa5BriAMDVRuQ4ZUxR9DnTIdqKs\nidTMbpc0CdgzrjrMzN7O9jrnnKsrORY451U3HbOkIcCXZjZJ0sDK9jEzk5R1nutc72xqAiwnVO/T\nNeyKc67RKdBV+52BAyXtB6wNrCPpTuALSR3NbI6kjsCX2Q6Uy1X7i4C7gU5AZ+C/kn6TV/jOOVdL\nUm6PbMzsN2bW2cy6EibEfMbMjgUeBobG3YYCD2U7Vi4l0uOBbcxsSXgT+jPwBvCXHF7rnHMF16Ru\n+5FeDtwr6STgU8J1oWrlkkjnVNivOK5zzrlEFLpDvpk9BzwXn88HBtXk9dUNWvJ3QpvoV8A7kp6I\ny4OBibUL1znn8hM65CcdxaqqK5GWX5l/B3gkY/2rdReOc85l0ZDGIzWzf9dnIM45l6uGeK99N0n3\nSHpL0oflj/oIzsGTTzzOVr170rtXd6684vKkw1lF2mJr03It/nvxAUz51wm8cfMwtt+8I78/ficm\n3HA8r/7zOMZc9jM6rtsy6TBT97lVlPb4yqv2BbxFNG+59Am9FfgPIf59gXuBUXUYk4vKyso45+wz\neWjMY7zx1ruMvudu3nv33aTDAtIZ21Wn786Tr8+g78n/YbvTb+f9z77i7/e9znan384OZ9zBY699\nwm+O3THRGNP4uWVKe3zlCjloSUHiyWGfFmb2BICZfWxmFxMSqqtjEydMoFu37myy6aY0a9aMw444\nkrFjsnZpqxdpi22dFs3YZcvO3Pr4VACWr/iBRd9+z+Ily1bu02LtpphlvUmlTqXtc6so7fFBHNi5\nASbS7+OgJR9LOk3SAax6X6qrI7Nnl9K584/ja5eUdKa0tDTBiH6Utti6btiGeYuWcNMv9+aV64/j\nn+cMpsVa4RLAiGE7M+3O4Ry5x+b88faXE4sR0ve5VZT2+MoVokN+IeWSSM8FWgJnE26pOgU4MduL\nJJ0t6T1Jd0k6UNKFcf0ISefnGqCkgZJ2ynX/2pLUS9KUOC5ht7o+nyus4iZF9O3egZvHvsmOZ97B\nku+Wc/4R2wEw4tbx9Dj2Ju555j1OO3CbhCN1hVBUpKyPeo0n2w5m9pqZLTazz8zsODM70MzG53Ds\nM4C9zOwYM3vYzGrbaj0QqDSRSirkLKgHA/eZ2TZm9nEuL5DUpIDnX02nTiXMmjVz5XJp6SxKSkrq\n8pQ5S1tspfMWUzp3MRM/+ByAB1/6kL7dVx20Z9Qz73HwLj2SCG+ltH1uFaU9PgCRvVqfmqq9pAcl\nPVDVo7qDSroR2BR4TNK5kobFkfYr7tdN0uOSJkl6UVKvCtu7AqcB58bS4q6SbpV0o6TXgCskbSfp\nlViSfFlSz/jaYTHWx+NI11fE9U3iMd6WNDXGtx9wDnC6pGfjfsdKmhDP+3/lSVPSN5L+JulNoE6v\nXPQfMICPPprGjOnTWbZsGaNH3cP+Qw6sy1PmLG2xfbFgCbPmLaZH5zCY+cC+G/H+Z/Pp1qntyn2G\n7NidD2d+lVSIQPo+t4rSHh8ABbrXvpCqK9HVemIfMztN0j7A7mY2T9KwKna9CTjNzKZJ2h74J7BH\nxnFmxKT8jZldBRDvf+1MGLm/LE4bvauZrZC0J3AZ8LN4iL7ANsD3wAeSrgM2AErMrE88XlszW5h5\nHkmbA0cAO5vZckn/BI4Bbic0c7xmZqtN3iNpODAcoMtG+U+yWlxczN//MZID9t+bsrIyhg47kS16\n9877uIWQxtjOu/4Z/vPr/WhW3IQZny9i+N8e54ZzB9Oj87r88IPx2Zdfc/a1TyUaYxo/t0xpj69c\nHd9rX2PVdch/ui5PLKkVoco+OqNz7Vo5vny0mZXF522A2yT1INzC2jRjv6fNbFE837vAxoQ7tTaN\nSfUR4MlKjj8I6AdMjLE158ehtMoIA12vJg4aexNAv379C3J5eJ9992OfffcrxKEKLm2xvfXJXHY5\n665V1h31xzEJRVO1tH1uFaU9PpG+DvmFbGOsqSJgoZn1rcVrv814/kfgWTP7aWwKeC5j2/cZz8uA\nYjNbIGlrYG9Cs8HhrH7xTMBtZlbZcIHfZSRx51wCUnaHaHKDNJvZ18B0SYcBKNi6kl0XU313qzZA\nef+MYdnOK6k9UGRm9wMXA9tWstvTwKGSNoivWVfSxtmO7ZyrHw3xziYAJOVa7a6JY4CT4oWbdwiT\nTlU0Bvhp+cWmSrZfAfxF0hvkVsIuAZ6TNAW4E1it1Glm7xKS7JOS3gLGAR1zeUPOubolhTmbsj3q\nU9bEI2k74N+Ekt9GsdR4spmdVd3r4qjT5c9vJdxqipmNyFg/Hdgny3E+BLbKWPVihe2vAJtlrLq4\n4jnj8pCMfVYrhWbGFZdHUcmtsGbWqrp4nXN1L2VNpDmVSK8FhgDzAczsTWD3ugzKOeeqEgYtSVc/\n0lyqwkVm9mmFq2R+scU5l5gmKSuR5pJIZ8bqvcVO6WcBPoyecy4RSqDEmU0uVfvTgfOAjYAvgB3i\nOuecS0Qh7myStHa8e/FNSe9IuiSuX1fSuHhH5DhJ7bIdK2uJ1My+JExV6pxziRNQXJir8t8De5jZ\nN5KaAi9Jegw4hHAzz+VxsKULgV9Xd6BcrtrfTLhjaBVmNrxWoTvnXJ4KUbO3MDjtN3GxaXwYoRvm\nwLj+NsJNPvklUiDz5uS1gZ8CM6vY1znn6lYBO9zH6z6TgO7A9Wb2mqQOZlY+5fznQIcqDxDlUrVf\npS+lpDuAl2oesnPO5U/kPGhJe0mvZyzfFMfDWCne7t1XUlvgQUl9Kmw3SVnHzajNvfabkEOGds65\nupJjiXSemfXPZcc4AtyzhBuEvpDU0czmSOrIjwMWVR1Pth0kLZD0VXwsJNwuWdlgHs45Vy8kZX3k\ncIz1Y0kUSc2BvYD3gYeBoXG3oUDWSauqLZEqRLM1Pw4K8oMlPXuYc65RC/faF+RQHQlDcDYhFCrv\nNbOxkl4B7o1jH39KGCGuWtUm0tg+8Gj5IMjOOZcGheiQb2ZvEQZ+r7h+PmFM4tzjyWGfKZJ8xjDn\nXCqEe+3TNYxelSVSScVmtoKQsSdK+pgwoLIIhdXKxvF0zrk6l7I7RKut2k8gDDeXspmv3JpswSOr\nTYWVKu/PXpx0CFW65MkPkg6hXgg1nDmbCCVPcp2a2Dnn6kUCVfdsqkuk60s6r6qNZnZ1HcTjnHNZ\npW30p+oSaROgFbFk6pxzaSCo96lEsqkukc4xs0vrLRLnnMtRygqk2dtInXMuTUSC0x9XobpEWqMO\nqc45Vy/UgNpIzeyr+gzEOedyUT75XZrUZvQn55xLVLrSqCdS51yDI4oa0FV755xLnYZ2sck551Ip\nl/FG61PaErur4MknHmer3j3p3as7V15xedLhrMJjy92I889gj2035dC9tl9t2+03Xcc2G6/Dgq/m\nJxBZUCT428FbcNHg7gC0WqsJf9h3M64/rA9/2HczWjZrklhsq4lX7bM96pMn0hQrKyvjnLPP5KEx\nj/HGW+8y+p67ee/dd5MOC/DYauqAw47h+tseWG3957Nn8eqLT7NhSZcEovrRkN4dmLVw6crlQ7bu\nyNTSrzlDWkXDAAAdp0lEQVRz9NtMLf2aQ7beMMHoVlVetc/2qE+eSFNs4oQJdOvWnU023ZRmzZpx\n2BFHMnZM1lkP6oXHVjP9tt+ZNm3brbb+qkt/wy9+88dEq6rrtWhKvy5teOqDeSvXbbdRW56dFkrI\nz06bz/Ybrx57kgox1UgheSJNsdmzS+nc+ceSSklJZ0pLS6t5Rf3x2PL37JOPsMGGHem5xZaJxnHi\njl24bcIsfshY17Z5MQuWLgdgwdLltG2ersspyuFRnxp1IpV0q6RDa7B/W0ln1GVMrnFYunQJt1x/\nFaefd1GicfTv0oZFS1fwyfwl1e6XponayqdjzvaoT+n6mUm/tsAZwD/r42SdOpUwa9bMlculpbMo\nKSmpj1Nn5bHlZ9an0ymd+SlH7LszAF/OKeXo/Xfljoeepf0G9Tfbea8OrRiwcVv6dWlD0yZFtGhW\nxDkDN2Hh0hW0a96UBUuX0655UxYtXVFvMeUiZRftG1eJVNLxkt6S9KakO+Lqn0h6WdIn5aVTSa0k\nPS1psqSpkg6K+14OdJM0RdKVdR1v/wED+OijacyYPp1ly5YxetQ97D8kHRMWeGz56dGrN89M/oRH\nx7/No+PfZoOOJfz3kRfrNYkC3Pl6Kafc/RanjprK3579hKmzF3PNc9OZ+NlCdu+xHgC791iPCZ8t\nrNe4qqec/st6FKmLpGclvSvpHUm/iOvXlTRO0rT4/6wNxI2mRCqpN3AxsJOZzZO0LnA1YUrWXYBe\nhPms7wO+A35qZl9Lag+8Kulh4EKgj5n1reIcw4HhAF022ijvmIuLi/n7P0ZywP57U1ZWxtBhJ7JF\n7955H7cQPLaaufCsE5j0ykssXDCfvbfvxWnn/pafHnl8ojFV54E353D+Ht0Y1LM9c79ZxlXPpGei\njPKqfQGsAH5pZpMltQYmSRoHDAOeNrPLJV1I+Hf/62pjaizT1Es6C9jQzC7KWHcrMM7M7orLi82s\ntaSmwN+BnwA/AD2BTYC1gbG5TE/dr19/G//a64V/Iy5RPmdT7fzvlAGTzKx/IY61WZ++dt2947Lu\nt0/vDWp0TkkPASPjY6CZzZHUEXjOzHpW99pGUyKtxvcZz8t/5o4B1gf6mdlySTMISdQ5lwI5Fkjb\nS8oszdxkZjdVfjx1JcyY/BrQwczmxE2fA1nbWxpTIn0GeFDS1WY2P1btq9IG+DIm0d2BjeP6xUDr\nug7UOVe1GlTt5+VSIpXUCrgfOCc2563cZmYmKWu1vdEkUjN7R9KfgecllQFvVLP7XcAYSVOB14H3\n4zHmSxov6W3gMTP7VZ0H7pxbTS4Xk3I6TmjGux+4y8zKbz37QlLHjKr9l9mO02gSKYCZ3QbcVs32\nVvH/84Adq9jn6LqJzjmXq0Jca1Ioev4beK/CrMgPA0MJvXSGAllvi2tUidQ51/AV8Kr9zsBxwFRJ\nU+K63xIS6L2STgI+BQ7PdiBPpM65Bia3fqLZmNlLVH03aY3mrPNE6pxrWJS+O5s8kTrnGpQCVu0L\nxhOpc67BSVca9UTqnGuIUpZJPZE65xqcQvUjLRRPpM65BidlszF7InXONUCeSJ1zrvbCVCLpyqSe\nSJ1zDYu8au+cc/nzROqcc/kozC2iheSJ1DnXoAiv2jvXoHVsm96JEh4f04imtvFE6pxz+fGqvXPO\n5cmr9s45lw/hVXvnnMuXV+2dcy4PftXeOecKIWWJtCjpAJxzrqaUw39ZjyHdIunLOL16+bp1JY2T\nNC3+v10u8Xgidc41OFL2Rw5uBfapsO5C4Gkz6wE8HZez8kTqnGtwCpFIzewF4KsKqw8CbovPbwMO\nziUebyN1zjUoNRhGr72kzNu9bjKzm7K8poOZzYnPPwc65HIiT6TOuYYl96r7PDPrX9vTmJlJslz2\n9ap9yj35xONs1bsnvXt158orLk86nFV4bLXXf8seDNxxGwbt0p/Bu+2QaCxtWjbjv7/eiynXH8Eb\nI49g+54dOGSnTZl03eF8++CpbNt9/UTjq0yB2kgr84WkjuEc6gh8mcuLvESaYmVlZZxz9pk88tg4\nSjp3ZpcdBjBkyIFsvsUWSYfmsRXA/WPHsd567ZMOg6tO3pknJ8/k6L+Oo2lxES3WKmbht99z5OVP\nMPL03ZIOrxJ1Oozew8BQ4PL4/4dyeZGXSFNs4oQJdOvWnU023ZRmzZpx2BFHMnZMTt9rnfPY1gzr\ntGjGLr07cuu49wFYvuIHFn27jA9mLWRa6aKEo6taIUqkku4GXgF6Spol6SRCAt1L0jRgz7iclZdI\nU2z27FI6d+6ycrmkpDMTJryWYEQ/8tjyI8ThB+1Dk6ImHHfCKRx3wsmJxNG1Q2vmLfqOm87enS03\nWY83Pp7L+TePZ8n3KxKJJxcir6r7SmZ2VBWbBtX0WKkqkUrqmtk5tp7O2V/StfV5TucefuJZnn7p\nde66fwz/+dcNvDL+xUTiKG5SRN9u7bn58XfY8dz7WPLdCs7/2TaJxFITheiQX0ipSqS5kNSkkMcz\ns9fN7OxCHrNQOnUqYdasmSuXS0tnUVJSkmBEP/LY8tOxU4hn/fU3YN8hB/HGpImJxFE67xtK533L\nxA/DNZUHX/6Yvt2Sb7fNpg4vNtVKGhNpsaS7JL0n6T5JLSTNkPRXSZOBwyT1lfSqpLckPSipnaQN\nJE0CkLS1JJO0UVz+OB7nMElvS3pT0gtx20BJY+PzEfG2seckfSJpZYKV9DtJH0h6SdLdks6v6w+i\n/4ABfPTRNGZMn86yZcsYPeoe9h9yYF2fNiceW+19++23fLN48crnzz/zFL226J1ILF8sXMqsed/Q\no6QNAAO36sz7MxckEkvO4iyi2R71KY1tpD2Bk8xsvKRbgDPi+vlmti2ApLeAs8zseUmXAn8ws3Mk\nrS1pHWBX4HVgV0kvAV+a2RJJvwf2NrNSSW2rOH8vYHegNfCBpBuAvsDPgK2BpsBkYFLFF0oaDgwH\n6LLRRnl/EMXFxfz9HyM5YP+9KSsrY+iwE9midzL/4Cry2Gpv3pdfcMKxhwGwYsUKDjn0SPbYc+/E\n4jnv5pf4z3mDaFbchBmff83wa5/lwB26cvUpu9C+TXMe+N2+vDV9PgeOeCSxGFeXrlFLZJZTf9N6\nIakr8IKZlZck9wDOJiSy3czsU0ltgKkZ+3QDRpvZtpJuBh4ATgDuJtxH+yKwlZldIOlGoBtwL/CA\nmc2XNBA438yGSBoBLDezP8djvwfsBRwKtDOzP8T1VwOzzeyqqt5Lv379bfxrjWgOnUZi0ZLlSYdQ\npa7H/TvpEKr03cOnT8qnc3ymrbfpZ48++0rW/Tq3W6tg58wmjVX7ipm9fPnbHF77AqE0ujGh/9fW\nwC6EZIqZnQZcDHQBJklar5JjfJ/xvIx0ltqda9SUw6M+pTGRbiRpx/j8aOClzI1mtghYIGnXuOo4\n4Pn4/EXgWGCamf1AGJBgv/JjSOpmZq+Z2e+BuYSEmovxwAGx6aAVMKR2b805VwhFUtZHvcZTr2fL\nzQfAmbFa3Q64oZJ9hgJXxrbSvsClAGY2g/Bj9ELc7yVgoZmVt55fKWlq7GL1MvBmLgGZ2UTCHQ9v\nAY8BU4H09lZ2bk2XsiJpqqq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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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dib6mqu9nd1CvSVVEJgLhQHPgWSAJpz/R07rIM8aYvCbg6zXTA34YTiUMqIcz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\nJ5OUlMT6daupUq162vadO+JITIin6dU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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "import itertools\n",
+ "\n",
+ "def plot_confusion_matrix(cm, classes,\n",
+ " normalize=False,\n",
+ " title='Confusion matrix',\n",
+ " fname='Confusion matrix',\n",
+ " cmap=plt.cm.Blues):\n",
+ " \"\"\"\n",
+ " This function prints and plots the confusion matrix.\n",
+ " Normalization can be applied by setting `normalize=True`.\n",
+ " \"\"\"\n",
+ " if normalize:\n",
+ " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+ " print(\"Normalized confusion matrix\")\n",
+ " else:\n",
+ " print('Confusion matrix, without normalization')\n",
+ "\n",
+ " print(cm)\n",
+ "\n",
+ " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+ " plt.title(title)\n",
+ " plt.colorbar()\n",
+ " tick_marks = np.arange(len(classes))\n",
+ " plt.xticks(tick_marks, classes, rotation=45)\n",
+ " plt.yticks(tick_marks, classes)\n",
+ "\n",
+ " fmt = '.1f' if normalize else 'd'\n",
+ " thresh = cm.max() / 2.\n",
+ " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+ " if normalize:\n",
+ " plt.text(j, i, format(cm[i, j]*100, fmt) + '%',\n",
+ " horizontalalignment=\"center\",\n",
+ " color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+ " else:\n",
+ " plt.text(j, i, format(cm[i, j], fmt),\n",
+ " horizontalalignment=\"center\",\n",
+ " color=\"white\" if cm[i, j] > thresh else \"black\") \n",
+ " \n",
+ " plt.tight_layout()\n",
+ " plt.ylabel('True label')\n",
+ " plt.xlabel('Predicted label')\n",
+ " plt.savefig(fname, bbox_inches='tight', pad_inches=1)\n",
+ "\n",
+ "# Compute confusion matrix\n",
+ "cnf_matrix = confusion_matrix(y_val_true, y_val_prediction)\n",
+ "np.set_printoptions(precision=2)\n",
+ "\n",
+ "# Plot non-normalized confusion matrix\n",
+ "plt.figure()\n",
+ "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
+ " title='Confusion matrix, without normalization',\n",
+ " fname=MODEL_NAME + \"_\" + 'Confusion_matrix_without_normalization')\n",
+ "\n",
+ "# Plot normalized confusion matrix\n",
+ "plt.figure()\n",
+ "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
+ " title='Normalized confusion matrix',\n",
+ " fname=MODEL_NAME + \"_\" + 'Normalized_confusion_matrix')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def stats(y_true, y_pred):\n",
+ " correct = sum([1 for i,pred in enumerate(y_pred) if y_true[i][pred]==1])\n",
+ " print(y_true.shape[0], correct, correct*1.0/len(y_true))\n",
+ " \n",
+ " for class_ind in range(y_true.shape[1]):\n",
+ " total_ind = len([1 for val in y_true if val[class_ind]==1])\n",
+ " correct_ind = sum([1 for i,pred in enumerate(y_pred) if (pred == class_ind and y_true[i][pred]==1)])\n",
+ " print(class_ind, total_ind, correct_ind, correct_ind*1.0/total_ind)\n",
+ "\n",
+ "stats(y_val, y_val_prediction)\n",
+ "# stats(y_test_vpn, y_test_vpn_prediction)\n",
+ "# stats(y_test_tor, y_test_tor_prediction)\n",
+ "# correct_1 = sum([1 for i,pred in enumerate(y_test_prediction) if (pred == 1 and y_test[i][pred]==1)])\n",
+ "# print correct_1, correct_1*1.0/len([1 for val in y_test if val[1]==1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0.0 1.00 0.97 0.98 61\n",
+ " 1.0 1.00 0.99 0.99 70\n",
+ " 2.0 0.97 1.00 0.98 63\n",
+ " 3.0 0.74 0.26 0.38 54\n",
+ " 4.0 0.60 0.92 0.73 66\n",
+ "\n",
+ "avg / total 0.86 0.85 0.83 314\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import classification_report\n",
+ "print(classification_report(y_val_true, y_val_prediction))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " ..., \n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 0 0]\n",
+ " [0 0 0 ..., 0 5 4]]\n",
+ "1.0\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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iAAAAg+opAACwaFWlerrBRBEAAIBBUAQAAGBQPQUAABZP9XQyUQQAAGAQFAEAABhUTwEA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\nkiRJkjTCQlGSJEmSNMJCUZIkSZI04r/kJaL0nEIc8QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convout1_1 output shape : (10, 300, 300)\n",
+ "convout1_1 output shape : (300, 300, 10)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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MV5dBa+3WeXb7fCv7Y6fbL5fLKZ1O96TsSc1/+8bGRmBq9ivrsVE/BplMZmvd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\nse6FYrHYchqvz130k9ftFYY7re2Up3b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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convout2_1 output shape : (20, 30, 30)\n",
+ "convout2_1 output shape : (30, 30, 20)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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CUEffryNZjLcs3kQY3sQ6aiG76ge9Fu+rcnuTVyg5vx1U0pw65CSGqJqqUxXL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\nJ3hfcfffMrMfmdmvVSsiAAAAANSDO3pvIqX0LTMbV0vv2+rnAgAAAAC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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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tla9V21TpAAq1Tx9//HFTU43NacaIGneq2dhb5qpVq551eZ5CoeAGB8SuPynv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\nAgAAAIAMvEwBAAAAQIb2dk0G3ejn/Zq4aqatqwlZNf95zb11NkbHit1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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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hls0zs/OS9THZTdwHDx6E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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize the first layer of convolutions on an input image\n",
+ "X = x_train[i][0]\n",
+ "print(X)\n",
+ "print(y_train_true[i])\n",
+ "pl.figure(figsize=(15, 15))\n",
+ "nice_imshow(pl.gca(), np.squeeze(X), vmin=0, vmax=1, cmap=cm.binary)\n",
+ "pl.savefig(MODEL_NAME + \"_input_\" + str(int(y_train_true[i])), bbox_inches='tight', pad_inches=1)\n",
+ "pl.show()\n",
+ "\n",
+ "# Visualize convolution result (after activation)\n",
+ "def get_layer_output(layer, input_img, layer_name):\n",
+ " convout_f = K.function(model.inputs, [layer.output])\n",
+ " C = convout_f([input_img])\n",
+ " C = np.squeeze(C)\n",
+ " print(layer_name + \" output shape : \", C.shape)\n",
+ " C = np.transpose(C)\n",
+ " C = np.swapaxes(C,0,1)\n",
+ " print(layer_name + \" output shape : \", C.shape)\n",
+ " return C\n",
+ "\n",
+ "\n",
+ "C1 = get_layer_output(convout1, x_train[i:i+1], layer_name=\"convout1_\" + str(int(y_train_true[i])))\n",
+ "mosaic_imshow(C1, 2, 5, cmap=cm.binary, border=2, layer_name=\"convout1_\" + str(int(y_train_true[i])))\n",
+ "plotNNFilter(C1, 2, 5, cmap=cm.binary, layer_name=\"convout1_\" + str(int(y_train_true[i])))\n",
+ "plotNNFilter2(C1, 2, 5, cmap=cm.binary, layer_name=\"convout1_\" + str(int(y_train_true[i])))\n",
+ "\n",
+ "C2 = get_layer_output(convout2, x_train[i:i+1], layer_name=\"convout2_\" + str(int(y_train_true[i])))\n",
+ "mosaic_imshow(C2, 4, 5, cmap=cm.binary, border=2, layer_name=\"convout2_\" + str(int(y_train_true[i])))\n",
+ "plotNNFilter(C2, 4, 5, cmap=cm.binary, layer_name=\"convout2_\" + str(int(y_train_true[i])))\n",
+ "plotNNFilter2(C2, 4, 5, cmap=cm.binary, layer_name=\"convout2_\" + str(int(y_train_true[i])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "W1 shape : (10, 10, 10)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py:861: RuntimeWarning: overflow encountered in true_divide\n",
+ " resdat /= (vmax - vmin)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Q1F/SWDPrn2hvAQAAAAA5p97G0t1nSdpUY/UoSQ9k7j8g6bJaooMklbt7hbvv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\nIVwzejxIUvv27UO5kpKScM3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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "W2 shape : (10, 10, 20)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py:860: RuntimeWarning: invalid value encountered in subtract\n",
+ " resdat -= vmin\n"
+ ]
+ },
+ {
+ "data": {
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AAAA0smAEAACgkZFUAACgq1ztI6mjiQ4jAAAAjUa0wzhmzJiYOHFiKjt58uRU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\njh2bf4m65ZZbUrlvfOMb6Zrz589PZ8+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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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7u9NtX19faXZXV1epb25uLvVnnHFGqX/66afT7b777luaPXr06HQ7cuTIhs9u\n3rw5brvttvSstWvXptuI39y/sxYvXlyaXbn/RERcdtllpf6kk04q9XPnzk23lftCRMSGDRvSbU9P\nT8Nnt23bFvfee2961imnnJJuIyKOPvrodDtx4sTS7FdeeaXUb9u2rdR/5CMfKfV/8zd/k27vuOOO\n0uwLLrig1H/xi19s+OzGjRvj29/+dnrWZz7zmXQbEXHOOeek22eeeaY0e+HChaW++hlhxIjady/P\nPvtsuj3++ONLs1euXJlu33zzzYbO+WYKAAAgwTIFAACQYJkCAABIsEwBAAAkWKYAAAASLFMAAAAJ\nlikAAIAEyxQAAECCZQoAACDBMgUAAJBgmQIAAEiwTAEAACRYpgAAABIsUwAAAAmWKQAAgISW4Rxu\namqK1tbW9LCDDjoo3UZEfPazn023n/70p0uzv/Wtb5X6j33sY6X+8ccfL/WLFi1Kt729vaXZt99+\ne7rdvHlzw2dHjhwZhx12WHpW9TEeO3Zsul2/fn1p9l577VXqu7q6Sv2jjz76e5u/ZMmS0uwjjjgi\n3e7evbvhs+PHj4/zzjsvPaujoyPdRkTMnDkz3VZeVxERP/3pT0v94sWLS/1tt91W6iuv7UobEXHa\naael2+uuu67hs/39/fHKK6+kZ910003pNqL2Op4yZUpp9qWXXlrq/8f/+B+lft26daV+wYIF6fYX\nv/hFafYZZ5xR6odj3LhxpddD9X327LPPTrfz5s0rzX7ttddK/ciRI0t9W1tbqT/zzDPT7YgRte99\nKu997e3tDZ3zzRQAAECCZQoAACDBMgUAAJBgmQIAAEiwTAEAACRYpgAAABIsUwAAAAmWKQAAgATL\nFAAAQIJlCgAAIMEyBQAAkGCZAgAASLBMAQAAJFimAAAAElqGc3jbtm3xwAMPpIe98cYb6TYiYtWq\nVen2iCOOKM3+q7/6q1K/ePHiUv+JT3yi1F9xxRXpdsKECaXZlWtm48aNDZ/dsmVL3H777elZU6dO\nTbcREc8//3y6nThxYmn2M888U+rPO++8Ut/b21vq99xzz3RbfW0fcMAB6bajo6Phs319ffHcc8+l\nZ7322mvpNiLiv//3/55uDz/88NLsU045pdSPGjWq1F966aWl/s/+7M/S7THHHFOaXXlt7d69u+Gz\nbW1tMXPmzPSstra2dBsRMWvWrHT77LPPlmavXLmy1P+n//SfSn31s9GGDRvS7bx580qzK6+N4err\n6yu91223fEsbAAAgAElEQVTfvr00//7770+3n/vc50qzL7nkklLf09NT6keMqH33smLFinRb+XwQ\nETFlypRS3wjfTAEAACRYpgAAABIsUwAAAAmWKQAAgATLFAAAQIJlCgAAIMEyBQAAkGCZAgAASLBM\nAQAAJFimAAAAEixTAAAACZYpAACABMsUAABAgmUKAAAgwTIFAACQ0DKcwzt37oxVq1alh3V1daXb\niIjzzjsv3d5///2l2U1NTaX+oosuKvVPPfVUqV+8eHG6feONN0qzp0+fnm6vueaahs+2trbG1KlT\n07MGBwfTbUTEpz71qXT7+c9/vjR79+7dpX706NGl/pBDDin1Tz75ZLrt6ekpzd6+fXu67e3tbfhs\ne3t77LvvvulZ1Wukr68v3VZfGz/60Y9KfX9/f6nfY489Sv3PfvazdPvss8+WZnd3d6fb4TxvI0eO\njIMPPjg965577km3EbV70BVXXFGafffdd5f6rVu3lvp3vOMdpf6WW25JtxdeeGFp9vz580v9Y489\n1vDZSZMmxcUXX5yeteeee6bbiIh//Md/TLfXX399afa4ceNK/X777Vfqq5+BK/fBRYsWlWZX338a\n4ZspAACABMsUAABAgmUKAAAgwTIFAACQYJkCAABIsEwBAAAkWKYAAAASLFMAAAAJlikAAIAEyxQA\nAECCZQoAACDBMgUAAJBgmQIAAEiwTAEAACRYpgAAABKahoaGGj/c1PRWRLz6b/fjwG+ZOTQ0NKmR\ng65Pfg9cn/whc33yh841yh+yhq7PYS1TAAAA/Ib/zA8AACDBMgUAAJBgmQIAAEiwTAEAACRYpgAA\nABIsUwAAAAktwznc2dk5NHHixPSwgYGBdBsRsXPnznT71ltvlWbPmjWr1L/55pulfvr06aW+ubk5\n3fb09JRmjxw5Mt2+8cYbsWXLlqZGzo4dO3Zo0qSG/rmK32nEiNrfFnbv3p1u+/v7S7Or/8RB9Xev\nzm9pGdat6A9m9ltvvRVbt25t6Prs7OwsXZ+jRo1KtxERvb296bajo6M0u9pX70HV63vHjh3ptr29\nvTS7cu9ev359bN68uaHrs6OjY6izszM9q9JGRGzfvj3dVj4bRNTeoyIitm3bVuqr18jbb7+dbqvP\n29SpU0v9ypUrNzb670x1dnYOTZgwIT3r9/k+OWbMmNLs1tbWUt/X11fqq5/fK/fwynMeUft89eab\nbzb0Hj+sTxETJ06MxYsXl36oilWrVqXba6+9tjT7yiuvLPXV+ZXHPaL2Ql6yZElp9sEHH5xuL7nk\nkobPTpo0Ka666qr0rLa2tnQbUfvA9fLLL5dmVxa5iPqHieqNtrJkVB73iN/c17I+9alPNXx20qRJ\n8fnPfz49a/78+ek2IuLxxx9Pt/PmzSvNfuc731nq77///lJfXURfeOGFdLvvvvuWZu+xxx7p9kMf\n+lDDZzs7O+Occ85Jz3r3u9+dbiMinnzyyXS7evXq0uxDDjmk1N93332lfsaMGaX+O9/5Tro94ogj\nSrMvv/zyUn/66ac3/I/wTpgwofRZqPo+VfmMUH19TJkypdQvX7681L/++uul/qGHHkq3H/7wh0uz\nK/eH//Jf/ktD5/xnfgAAAAmWKQAAgATLFAAAQIJlCgAAIMEyBQAAkGCZAgAASLBMAQAAJFimAAAA\nEixTAAAACZYpAACABMsUAABAgmUKAAAgwTIFAACQ0DKcw6NGjYoFCxbkh7UMa9xvWbt2bbq96qqr\nSrPXrFlT6r///e+X+mXLlpX6J598Mt2eddZZpdkjRuR39ra2tobPtrS0xIQJE9KzRo0alW4jIm6/\n/fZ0e9BBB5Vmd3d3l/o333yz1FcNDQ2l21mzZpVmT548Od22t7c3fHZoaCh27tyZnrVly5Z0GxHR\n0dGRblesWFGa/fLLL5f65557rtRv37691F900UXptvrYDece+M/t3r274bPjxo2LM888Mz1rxowZ\n6TYioqurK92eeOKJpdmvvPJKqV+0aFGpf+mll0r9Bz7wgXQ7MDBQml25dw/X7t27S6/lwcHB0vz9\n998/3VY/Q1Y+w0VEHHzwwaX+F7/4Ral/73vfm26rP/vy5cvTbaP3UN9MAQAAJFimAAAAEixTAAAA\nCZYpAACABMsUAABAgmUKAAAgwTIFAACQYJkCAABIsEwBAAAkWKYAAAASLFMAAAAJlikAAIAEyxQA\nAECCZQoAACDBMgUAAJDQMpzDmzZtiu9///vpYUcddVS6jYj4yEc+km4/+tGPlma//PLLpf78888v\n9V1dXaV+7Nix6Xb79u2l2RMmTEi3TU1NDZ/dtWtXdHd3p2etXbs23UZE9Pb2ptuBgYHS7FGjRpX6\nD3/4w6X+hRdeKPW7du1KtytXrizN3nPPPdPtcK7PPfbYIz74wQ+mZ7344ovpNiLilFNOSbdbtmwp\nzV62bFmpnz9/fqmfPXt2qX/sscfS7Y4dO0qzzzjjjHQ7nPvC0NBQ9PX1pWd94xvfSLcRtfeo//gf\n/2Np9syZM0v9YYcdVuqffPLJUv/pT3863W7cuLE0e9asWaV+OEaPHh0LFixI92vWrCnNX7x4cbqt\n/NwREXPmzCn11d993Lhxpf5zn/tcuv34xz9emn3vvfem223btjV0zjdTAAAACZYpAACABMsUAABA\ngmUKAAAgwTIFAACQYJkCAABIsEwBAAAkWKYAAAASLFMAAAAJlikAAIAEyxQAAECCZQoAACDBMgUA\nAJBgmQIAAEhoGdbhlpaYMGFCetjkyZPTbUTEQw89lG57enpKs9evX1/qN2/eXOoXLlxY6qdPn55u\n77vvvtLsBQsWpNvBwcGGz/b29sYzzzyTnrVly5Z0GxHR19eXbquvjW984xul/h3veEepr74+1qxZ\nk24POOCA0uzRo0en2xEjGv971NatW+Oee+5Jz5ozZ066jYhYvXp1ul2+fHlp9rRp00r9vffeW+pb\nWob1VvdbzjvvvHS7atWq0uxdu3aV+kaNGTMmjjvuuHS/Y8eO0vzKe1T1/nnJJZeU+h/96Eel/swz\nzyz1lffo7du3l2b/yZ/8Sakfjup7fOXza0TEokWL0u2pp55amv29732v1D/77LOlfsqUKaX+u9/9\nbrptb28vzX799dfT7V133dXQOd9MAQAAJFimAAAAEixTAAAACZYpAACABMsUAABAgmUKAAAgwTIF\nAACQYJkCAABIsEwBAAAkWKYAAAASLFMAAAAJlikAAIAEyxQAAECCZQoAACDBMgUAAJDQMpzD7e3t\nMWfOnPSwrVu3ptuIiKOPPjrdXn/99aXZRxxxRKnv7u4u9Q8//HCpX7RoUbrduHFjafYDDzyQbrdt\n29bw2e7u7vjBD36QnnXooYem24iITZs2pdsPfehDpdnveMc7Sv19991X6tetW1fqK4/dqFGjSrPb\n2trSbV9fX8NnW1paYuLEielZLS3Dul3/lnHjxqXbk046qTT7+eefL/XnnntuqX/ppZdKfVNTU7qt\nvjZmzJiRboeGhho+u3379nj00UfTs6rvkU8//XS6Pfnkk0uzly5dWuqrr48VK1aU+spj//LLL5dm\nL1++vNQPx+7du2P79u3pftW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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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sAwAAAICGsNgHAAAAAA3hGC8AAAAAbVNKcYy3Rnb2AQAA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\nAAAADWGxDwAAAAAawmIfAAAAADSEe/YBAAAA0Da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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize weights\n",
+ "W1 = model.layers[0].get_weights()[0]\n",
+ "W1 = np.squeeze(W1)\n",
+ "# W1 = np.asarray(W1)\n",
+ "print(\"W1 shape : \", W1.shape)\n",
+ "\n",
+ "mosaic_imshow(W1, 2, 5, cmap=cm.binary, border=1, layer_name=\"conv1_weights\")\n",
+ "plotNNFilter(W1, 2, 5, cmap=cm.binary, layer_name=\"conv1_weights\")\n",
+ "plotNNFilter2(W1, 2, 5, cmap=cm.binary, layer_name=\"conv1_weights\")\n",
+ "\n",
+ "# Visualize weights\n",
+ "W2 = model.layers[3].get_weights()[0][:,:,0,:]\n",
+ "W2 = np.asarray(W2)\n",
+ "print(\"W2 shape : \", W2.shape)\n",
+ "\n",
+ "mosaic_imshow(W2, 4, 5, cmap=cm.binary, border=1, layer_name=\"conv2_weights\")\n",
+ "plotNNFilter(W2, 4, 5, cmap=cm.binary, layer_name=\"conv2_weights\")\n",
+ "plotNNFilter2(W2, 4, 5, cmap=cm.binary, layer_name=\"conv2_weights\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_val_true, y_val_prediction\n",
+ "\n",
+ "for j in range(len(y_val_true)):\n",
+ " if y_val_true[j] == 0 and y_val_prediction[j] == 1:\n",
+ " print(j, sum(sum(sum(x_val[j]))))\n",
+ "# pl.figure(figsize=(10, 10))\n",
+ "# pl.title('input ' + str(j))\n",
+ "# nice_imshow(pl.gca(), np.squeeze(x_val[j]), vmin=0, vmax=1, cmap=cm.binary)\n",
+ "# pl.savefig(MODEL_NAME + \"_input\", bbox_inches='tight', pad_inches=1)\n",
+ "# pl.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1124, 1, 1500, 1500) (1124, 5)\n",
+ "(2464, 1, 1500, 1500) (2464, 5)\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_test_vpn = np.load(PATH_PREFIX + \"vpn_x_test.npy\")\n",
+ "y_test_vpn_true = np.load(PATH_PREFIX + \"vpn_y_test.npy\")\n",
+ "x_test_tor = np.load(PATH_PREFIX + \"tor_x_test.npy\")\n",
+ "y_test_tor_true = np.load(PATH_PREFIX + \"tor_y_test.npy\")\n",
+ "\n",
+ "y_test_vpn = np_utils.to_categorical(y_test_vpn_true, num_classes)\n",
+ "y_test_tor = np_utils.to_categorical(y_test_tor_true, num_classes)\n",
+ "\n",
+ "print(x_test_vpn.shape, y_test_vpn.shape)\n",
+ "print(x_test_tor.shape, y_test_tor.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Evaluate model on test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "314/314 [==============================] - 29s \n",
+ "Validation loss: 0.336662959066\n",
+ "Validaion accuracy: 0.847133757962\n",
+ "Validaion top_2_categorical_accuracy: 0.993630573248\n",
+ "1124/1124 [==============================] - 102s \n",
+ "VPN_Test loss: 4.94784528505\n",
+ "VPN_Test accuracy: 0.556939501779\n",
+ "VPN_Test top_2_categorical_accuracy: 0.965302491103\n",
+ "2464/2464 [==============================] - 224s \n",
+ "TOR_Test loss: 5.39122196528\n",
+ "TOR_Test accuracy: 0.387581168831\n",
+ "TOR_Test top_2_categorical_accuracy: 0.735795454545\n"
+ ]
+ }
+ ],
+ "source": [
+ "# model.load_weights(MODEL_NAME + '.hdf5')\n",
+ "\n",
+ "score_val = model.evaluate(x_val, y_val, verbose=1)\n",
+ "print('Validation loss:', score_val[0])\n",
+ "print('Validaion accuracy:', score_val[1])\n",
+ "print('Validaion top_2_categorical_accuracy:', score_val[2])\n",
+ "\n",
+ "score_vpn = model.evaluate(x_test_vpn, y_test_vpn, verbose=1)\n",
+ "print('VPN_Test loss:', score_vpn[0])\n",
+ "print('VPN_Test accuracy:', score_vpn[1])\n",
+ "print('VPN_Test top_2_categorical_accuracy:', score_vpn[2])\n",
+ "\n",
+ "score_tor = model.evaluate(x_test_tor, y_test_tor, verbose=1)\n",
+ "print('TOR_Test loss:', score_tor[0])\n",
+ "print('TOR_Test accuracy:', score_tor[1])\n",
+ "print('TOR_Test top_2_categorical_accuracy:', score_tor[2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1124/1124 [==============================] - 104s \n",
+ "2464/2464 [==============================] - 242s \n"
+ ]
+ }
+ ],
+ "source": [
+ "y_test_vpn_prediction = model.predict_classes(x_test_vpn, verbose=1)\n",
+ "y_test_tor_prediction = model.predict_classes(x_test_tor, verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Normalized confusion matrix\n",
+ "[[ 0.97 0. 0.03 0. 0. ]\n",
+ " [ 0. 0.99 0. 0. 0.01]\n",
+ " [ 0. 0. 1. 0. 0. ]\n",
+ " [ 0. 0. 0. 0.26 0.74]\n",
+ " [ 0. 0. 0. 0.08 0.92]]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in true_divide\n",
+ " \n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Normalized confusion matrix\n",
+ "[[ 0.99 0. 0.01 0. 0. ]\n",
+ " [ 0. 0.01 0. 0. 0.99]\n",
+ " [ 0.01 0.01 0.57 0.27 0.14]\n",
+ " [ 0.02 0. 0.1 0.65 0.22]\n",
+ " [ nan nan nan nan nan]]\n",
+ "Normalized confusion matrix\n",
+ "[[ 0. 0.26 0.05 0.52 0.16]\n",
+ " [ 0. 0.73 0.03 0.03 0.2 ]\n",
+ " [ 0.1 0.48 0.38 0.01 0.03]\n",
+ " [ 0. 0.02 0. 0.31 0.67]\n",
+ " [ 0.01 0.09 0.1 0.24 0.55]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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XAqDk7UVpd381Fn2znWMn/+HemuUBaFq7ApFHT15T76/T/xL11xkq3Gn5FZvW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\nEO4okTltVUoXo9/D1bl36DzqDJxDy3r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EyGI4McVe6JtJyXTZT1KfEhMeD5wn6VfllCnA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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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Oh51n+FPP0OS2226YHldd3y5eGq/r7UlvMnfBtzzx+ADCzocx7KkRNG5y43W56vPGcvNK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\no4E5JJ0AYPufwASiyG8tkbScpDnL412IPeCHAus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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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gXF1dCQkJ4fHjxzg7OxMeHs70qd/w/tDhDtHt9QqF+Mc/mMtBt7n/8FFUeNo0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\nuYTbBBEPeXBJeh7FbXjoH9L3JAAz2+N9qOHFK1VPtkauJEpTXKxlQVx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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cnf_matrix_val = confusion_matrix(y_val_true, y_val_prediction)\n",
+ "cnf_matrix_vpn = confusion_matrix(y_test_vpn_true, y_test_vpn_prediction)\n",
+ "cnf_matrix_tor = confusion_matrix(y_test_tor_true, y_test_tor_prediction)\n",
+ "\n",
+ "# Plot normalized confusion matrix\n",
+ "plt.figure()\n",
+ "plot_confusion_matrix(cnf_matrix_val, classes=class_names, normalize=True,\n",
+ " title='Normalized confusion matrix for regular validation set',\n",
+ " fname=MODEL_NAME + \"_val_\" + 'Normalized_confusion_matrix')\n",
+ "\n",
+ "# Plot normalized confusion matrix\n",
+ "plt.figure()\n",
+ "plot_confusion_matrix(cnf_matrix_vpn, classes=class_names, normalize=True,\n",
+ " title='Normalized confusion matrix for vpn test set',\n",
+ " fname=MODEL_NAME + \"_test_vpn_\" + 'Normalized_confusion_matrix')\n",
+ "\n",
+ "# Plot normalized confusion matrix\n",
+ "plt.figure()\n",
+ "plot_confusion_matrix(cnf_matrix_tor, classes=class_names, normalize=True,\n",
+ " title='Normalized confusion matrix for tor test set',\n",
+ " fname=MODEL_NAME + \"_test_tor_\" + 'Normalized_confusion_matrix')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Save Model and weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Save Model\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_json = model.to_json()\n",
+ "with open(MODEL_NAME + '.json', \"w\") as json_file:\n",
+ " json_file.write(model_json)\n",
+ "model.save_weights(MODEL_NAME + '.h5')\n",
+ "print(\"Save Model\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/_reference/sessions_plotter.py b/_reference/sessions_plotter.py
new file mode 100644
index 0000000..aaad287
--- /dev/null
+++ b/_reference/sessions_plotter.py
@@ -0,0 +1,42 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+MTU = 1500
+
+
+def session_spectogram(ts, sizes, name=None):
+ plt.scatter(ts, sizes, marker='.')
+ plt.ylim(0, MTU + 100)
+ plt.xlim(ts[0], ts[-1])
+ # plt.yticks(np.arange(0, MTU, 10))
+ # plt.xticks(np.arange(int(ts[0]), int(ts[-1]), 10))
+ plt.title(name + " Session Spectogram")
+ plt.ylabel('Size [B]')
+ plt.xlabel('Time [sec]')
+
+ plt.grid(True)
+ plt.show()
+
+
+def session_histogram(sizes, plot=False):
+ hist, bin_edges = np.histogram(sizes, bins=range(0, MTU + 1, 1))
+ if plot:
+ plt.bar(bin_edges[:-1], hist, width=1)
+ plt.xlim(min(bin_edges), max(bin_edges)+100)
+ plt.show()
+ return hist.astype(np.uint16)
+
+
+def session_2d_histogram(ts, sizes, plot=False):
+ # ts_norm = map(int, ((np.array(ts) - ts[0]) / (ts[-1] - ts[0])) * MTU)
+ ts_norm = ((np.array(ts) - ts[0]) / (ts[-1] - ts[0])) * MTU
+ H, xedges, yedges = np.histogram2d(sizes, ts_norm, bins=(range(0, MTU + 1, 1), range(0, MTU + 1, 1)))
+
+ if plot:
+ plt.pcolormesh(xedges, yedges, H)
+ plt.colorbar()
+ plt.xlim(0, MTU)
+ plt.ylim(0, MTU)
+ plt.set_cmap('binary')
+ plt.show()
+ return H.astype(np.uint16)