深度神经网络：实例探究-week2编程题2（残差网络的搭建）

 1 def identity_block(X, f, filters, stage, block):
 2     """
 3     Implementation of the identity block as defined in Figure 3
 4     
 5     Arguments:
 6     X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
 7     f -- integer, specifying the shape of the middle CONV‘s window for the main path
 8     filters -- python list of integers, defining the number of filters in the CONV layers of the main path
 9     stage -- integer, used to name the layers, depending on their position in the network
10     block -- string/character, used to name the layers, depending on their position in the network
11     
12     Returns:
13     X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
14     """
15     # defining name basis
16     conv_name_base = ‘res‘ + str(stage) + block + ‘_branch‘
17     bn_name_base = ‘bn‘ + str(stage) + block + ‘_branch‘
18     
19     # Retrieve Filters
20     F1, F2, F3 = filters
21     
22     # Save the input value. You‘ll need this later to add back to the main path. 
23     X_shortcut = X
24     
25     # First component of main path
26     X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = ‘valid‘, name = conv_name_base + ‘2a‘, kernel_initializer = glorot_uniform(seed=0))(X)
27     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2a‘)(X)
28     X = Activation(‘relu‘)(X)
29     
30     ### START CODE HERE ###    
31     # Second component of main path (≈3 lines)
32     X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = ‘same‘, name = conv_name_base + ‘2b‘, kernel_initializer = glorot_uniform(seed=0))(X)
33     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2b‘)(X)
34     X = Activation(‘relu‘)(X)
35     
36     # Third component of main path (≈2 lines)
37     X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = ‘valid‘, name = conv_name_base + ‘2c‘, kernel_initializer = glorot_uniform(seed=0))(X)
38     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2c‘)(X)
39 
40     # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
41     X = Add()([X,X_shortcut])
42     X = Activation("relu")(X)  
43     ### END CODE HERE ###
44     
45     return X

 1 def convolutional_block(X, f, filters, stage, block, s = 2):
 2     """
 3     Implementation of the convolutional block as defined in Figure 4
 4     
 5     Arguments:
 6     X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
 7     f -- integer, specifying the shape of the middle CONV‘s window for the main path
 8     filters -- python list of integers, defining the number of filters in the CONV layers of the main path
 9     stage -- integer, used to name the layers, depending on their position in the network
10     block -- string/character, used to name the layers, depending on their position in the network
11     s -- Integer, specifying the stride to be used
12     
13     Returns:
14     X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
15     """
16     # defining name basis
17     conv_name_base = ‘res‘ + str(stage) + block + ‘_branch‘
18     bn_name_base = ‘bn‘ + str(stage) + block + ‘_branch‘
19     
20     # Retrieve Filters
21     F1, F2, F3 = filters
22     
23     # Save the input value
24     X_shortcut = X
25 
26     ##### MAIN PATH #####
27     # First component of main path 
28     X = Conv2D(F1, (1, 1), strides = (s,s),padding = ‘valid‘, name = conv_name_base + ‘2a‘, kernel_initializer = glorot_uniform(seed=0))(X)
29     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2a‘)(X)
30     X = Activation(‘relu‘)(X)
31     
32     ### START CODE HERE ###
33     # Second component of main path (≈3 lines)
34     X = Conv2D(F2, (f, f), strides = (1,1), padding = ‘same‘, name = conv_name_base + ‘2b‘, kernel_initializer = glorot_uniform(seed=0))(X)
35     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2b‘)(X)
36     X = Activation(‘relu‘)(X)
37     
38     # Third component of main path (≈2 lines)
39     X = Conv2D(F3, (1, 1), strides = (1,1), padding = ‘valid‘, name = conv_name_base + ‘2c‘, kernel_initializer = glorot_uniform(seed=0))(X)
40     X = BatchNormalization(axis = 3, name = bn_name_base + ‘2c‘)(X)
41     
42     ##### SHORTCUT PATH #### (≈2 lines)
43     X_shortcut=Conv2D(F3,(1,1), strides=(s,s), padding=‘valid‘, name=conv_name_base + ‘1‘, kernel_initializer = glorot_uniform(seed=0))(X_shortcut)  
44     X_shortcut=BatchNormalization(axis=3,name=bn_name_base + ‘1‘)(X_shortcut)
45     
46     # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
47     X = Add()([X,X_shortcut])
48     X = Activation("relu")(X) 
49     ### END CODE HERE ###
50     
51     return X

 1 def ResNet50(input_shape = (64, 64, 3), classes = 6):
 2     """
 3     Implementation of the popular ResNet50 the following architecture:
 4     CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
 5     -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER
 6 
 7     Arguments:
 8     input_shape -- shape of the images of the dataset
 9     classes -- integer, number of classes
10 
11     Returns:
12     model -- a Model() instance in Keras
13     """    
14     # Define the input as a tensor with shape input_shape
15     X_input = Input(input_shape)
16     
17     # Zero-Padding
18     X = ZeroPadding2D((3, 3))(X_input)
19     
20     # Stage 1
21     X = Conv2D(64, (7, 7), strides = (2, 2), name = ‘conv1‘, kernel_initializer = glorot_uniform(seed=0))(X)
22     X = BatchNormalization(axis = 3, name = ‘bn_conv1‘)(X)
23     X = Activation(‘relu‘)(X)
24     X = MaxPooling2D((3, 3), strides=(2, 2))(X)
25 
26     # Stage 2
27     X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block=‘a‘, s = 1)
28     X = identity_block(X, 3, [64, 64, 256], stage=2, block=‘b‘)
29     X = identity_block(X, 3, [64, 64, 256], stage=2, block=‘c‘)
30 
31     ### START CODE HERE ###
32 
33     # Stage 3 (≈4 lines)
34     X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block=‘a‘, s = 2)
35     X = identity_block(X, 3, [128, 128, 512], stage=3, block=‘b‘)
36     X = identity_block(X, 3, [128, 128, 512], stage=3, block=‘c‘)
37     X = identity_block(X, 3, [128, 128, 512], stage=3, block=‘d‘)
38     
39     # Stage 4 (≈6 lines)
40     X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block=‘a‘, s = 3)
41     X = identity_block(X, 3, [256, 256, 1024], stage=4, block=‘b‘)
42     X = identity_block(X, 3, [256, 256, 1024], stage=4, block=‘c‘)
43     X = identity_block(X, 3, [256, 256, 1024], stage=4, block=‘d‘)
44     X = identity_block(X, 3, [256, 256, 1024], stage=4, block=‘e‘)
45     X = identity_block(X, 3, [256, 256, 1024], stage=4, block=‘f‘)
46     
47     # Stage 5 (≈3 lines)
48     X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block=‘a‘, s = 4)
49     X = identity_block(X, 3, [512, 512, 2048], stage=5, block=‘b‘)
50     X = identity_block(X, 3, [512, 512, 2048], stage=5, block=‘c‘)
51     
52     # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
53     X = AveragePooling2D(pool_size=(2,2),padding="same")(X)
54     ### END CODE HERE ###
55 
56     # output layer
57     X = Flatten()(X)
58     X = Dense(classes, activation=‘softmax‘, name=‘fc‘ + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
59     
60     # Create model
61     model = Model(inputs = X_input, outputs = X, name=‘ResNet50‘)
62 
63     return model

1 X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
2 
3 # Normalize image vectors
4 X_train = X_train_orig/255.      #(1080,64,64,3)
5 X_test = X_test_orig/255.        #(120,64,64,3)
6 
7 # Convert training and test labels to one hot matrices
8 Y_train = convert_to_one_hot(Y_train_orig, 6).T        #(1080,6)
9 Y_test = convert_to_one_hot(Y_test_orig, 6).T          #(120,6)

1 model = ResNet50(input_shape = (64, 64, 3), classes = 6)
2 model.compile(optimizer=‘adam‘, loss=‘categorical_crossentropy‘, metrics=[‘accuracy‘])
3 model.fit(X_train, Y_train, epochs = 2, batch_size = 32)
4 
5 preds = model.evaluate(X_test, Y_test)
6 print ("Loss = " + str(preds[0]))
7 print ("Test Accuracy = " + str(preds[1]))

1 import keras
2 keras.backend.clear_session()
3 model = load_model(‘ResNet50.h5‘,compile=False)
4 model.compile(optimizer=‘adam‘, loss=‘categorical_crossentropy‘, metrics=[‘accuracy‘]) 
5 preds = model.evaluate(X_test, Y_test)
6 print ("Loss = " + str(preds[0]))
7 print ("Test Accuracy = " + str(preds[1]))