/
CNN.py
130 lines (109 loc) · 4.73 KB
/
CNN.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
# -*- coding: utf-8 -*-
"""Project1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1JPwLX1tk4Vhcrsra0icoGpJJD5QrDTz9
"""
from google.colab import drive
drive.mount('/content/gdrive/',force_remount=True)
import sys
import os
prefix = '/content/gdrive/My Drive/'
# modify "customized_path_to_your_homework" here to where you uploaded your homework
customized_path_to_your_homework = 'project'
sys_path = os.path.join(prefix, customized_path_to_your_homework)
sys.path.append(sys_path)
print(sys_path)
import pickle
import numpy as np
pickle_in=open(sys_path+'/X_train.pickle','rb')
X_train=pickle.load(pickle_in)
X_train = X_train/255.0
pickle_in=open(sys_path+'/X_validation.pickle','rb')
X_validation=pickle.load(pickle_in)
X_validation = X_validation/255.0
pickle_in=open(sys_path+'/y_train.pickle','rb')
Y_train=pickle.load(pickle_in)
pickle_in=open(sys_path+'/y_validation.pickle','rb')
Y_validation=np.array(pickle.load(pickle_in))
# print(np.shape(Y_train))
# print(np.shape(X_train))
# print(np.shape(Y_validation[:,1]))
# print(np.shape(X_validation))
import tensorflow
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Conv2D, Activation, MaxPooling2D, Dense, Flatten, Dropout
from keras.preprocessing.image import ImageDataGenerator
from IPython.display import display
from PIL import Image
from keras.models import load_model
import numpy as np
from sklearn import metrics
print(tensorflow.__version__)
classifier = Sequential()
classifier.add(Conv2D(64, (3, 3), input_shape=(150, 150, 3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Conv2D(64, (3, 3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Conv2D(64, (3, 3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
classifier.add(Flatten())
classifier.add(Dense(32))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(1))
classifier.add(Activation('sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifier.summary()
classifier.fit(X_train, Y_train, epochs=20, batch_size=32, validation_split=0)
# k = classifier.fit(X_train, Y_train, epochs=20, batch_size=32, validation_split=30)
# y_pred=classifier.predict(X_validation)
# print("Accuracy:",metrics.accuracy_score(Y_validation, y_pred))
# predictions = model.predict(X_test)
# predicted_val = [int(round(p[0])) for p in predictions]
# train_datagen = ImageDataGenerator(rescale=1. / 255,
# shear_range=0.2,
# zoom_range=0.2,
# horizontal_flip=True)
# test_datagen = ImageDataGenerator(rescale=1. / 255)
# training_set = train_datagen.flow_from_directory('project1/train',
# target_size=(150, 150),
# batch_size=32,
# class_mode='binary')
# test_set = test_datagen.flow_from_directory('project1/validation',
# target_size=(150, 150),
# batch_size=32,
# class_mode='binary')
# classifier.fit_generator(X_train,
# steps_per_epoch=625,
# epochs=30,
# validation_data=X_validation,
# validation_steps=5000)
# classifier.save('catdog_cnn_model.h5')
# classifier = load_model('catdog_cnn_model.h5')
# classifier.fit(X_train, y_train)
# y_pred=classifier.predict(X_test)
# k.history.keys()
scores = classifier.evaluate(X_validation, Y_validation, verbose=0)
print("%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100))
# print("%s: %.2f%%" % (classifier.metrics_names[0], scores[0]*100))
from keras.models import Model
import matplotlib.pyplot as plt
layer_outputs = [layer.output for layer in classifier.layers]
activation_model = Model(inputs=classifier.input, outputs=layer_outputs)
activations = activation_model.predict(X_train[2].reshape(1,150, 150, 3))
def display_activation(activations, col_size, row_size, act_index):
activation = activations[act_index]
activation_index=0
fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5))
for row in range(0,row_size):
for col in range(0,col_size):
ax[row][col].imshow(activation[0, :, :, activation_index], cmap='viridis')
activation_index += 1
display_activation(activations, 8, 8, 0)
display_activation(activations, 8, 8, 3)
display_activation(activations, 8, 8, 6)