Coursera-Deep Learning Specialization 课程之（四）：Convolutional Neural Networks: -weak2编程作业

Residual Networks

1 - The problem of very deep neural networks

2 - Building a Residual Network

2.1 - The identity block

def identity_block(X, f, filters, stage, block):    """    Implementation of the identity block as defined in Figure 3    Arguments:    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)    f -- integer, specifying the shape of the middle CONV's window for the main path    filters -- python list of integers, defining the number of filters in the CONV layers of the main path    stage -- integer, used to name the layers, depending on their position in the network    block -- string/character, used to name the layers, depending on their position in the network    Returns:    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)    """    # defining name basis    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    # Retrieve Filters    F1, F2, F3 = filters    # Save the input value. You'll need this later to add back to the main path.     X_shortcut = X    # First component of main path    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)    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)    X = Activation('relu')(X)    ### START CODE HERE ###    # Second component of main path (≈3 lines)    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)    X =  BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)    X =  Activation('relu')(X)    # Third component of main path (≈2 lines)    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)    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)    X = Add()([X,X_shortcut])    X = Activation('relu')(X)    ### END CODE HERE ###    return X

tf.reset_default_graph()with tf.Session() as test:    np.random.seed(1)    A_prev = tf.placeholder("float", [3, 4, 4, 6])    X = np.random.randn(3, 4, 4, 6)    A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')    test.run(tf.global_variables_initializer())    out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})    print("out = " + str(out[0][1][1][0]))

out = [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003]

2.2 - The convolutional block

# GRADED FUNCTION: convolutional_blockdef convolutional_block(X, f, filters, stage, block, s = 2):    """    Implementation of the convolutional block as defined in Figure 4    Arguments:    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)    f -- integer, specifying the shape of the middle CONV's window for the main path    filters -- python list of integers, defining the number of filters in the CONV layers of the main path    stage -- integer, used to name the layers, depending on their position in the network    block -- string/character, used to name the layers, depending on their position in the network    s -- Integer, specifying the stride to be used    Returns:    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)    """    # defining name basis    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    # Retrieve Filters    F1, F2, F3 = filters    # Save the input value    X_shortcut = X    ##### MAIN PATH #####    # First component of main path     X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)    X = Activation('relu')(X)    ### START CODE HERE ###    # Second component of main path (≈3 lines)    X = Conv2D(F2, (f, f), strides = (1,1), padding = 'same',name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)    X = Activation('relu')(X)    # Third component of main path (≈2 lines)    X = Conv2D(F3, (1, 1), strides = (1,1), name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)    ##### SHORTCUT PATH #### (≈2 lines)    X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '1', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)    X = Add()([X,X_shortcut])    X = Activation('relu')(X)    ### END CODE HERE ###    return X

tf.reset_default_graph()with tf.Session() as test:    np.random.seed(1)    A_prev = tf.placeholder("float", [3, 4, 4, 6])    X = np.random.randn(3, 4, 4, 6)    A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')    test.run(tf.global_variables_initializer())    out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})    print("out = " + str(out[0][1][1][0]))

out = [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603]

3 - Building your first ResNet model (50 layers)

# GRADED FUNCTION: ResNet50def ResNet50(input_shape = (64, 64, 3), classes = 6):    """    Implementation of the popular ResNet50 the following architecture:    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER    Arguments:    input_shape -- shape of the images of the dataset    classes -- integer, number of classes    Returns:    model -- a Model() instance in Keras    """    # Define the input as a tensor with shape input_shape    X_input = Input(input_shape)    # Zero-Padding    X = ZeroPadding2D((3, 3))(X_input)    # Stage 1    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)    X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)    X = Activation('relu')(X)    X = MaxPooling2D((3, 3), strides=(2, 2))(X)    # Stage 2    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')    ### START CODE HERE ###    # Stage 3 (≈4 lines)    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')    # Stage 4 (≈6 lines)    X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')    # Stage 5 (≈3 lines)    X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"    X = AveragePooling2D(pool_size=(2, 2))(X)    ### END CODE HERE ###    # output layer    X = Flatten()(X)    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)    # Create model    model = Model(inputs = X_input, outputs = X, name='ResNet50')    return model

model = ResNet50(input_shape = (64, 64, 3), classes = 6)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Normalize image vectorsX_train = X_train_orig/255.X_test = X_test_orig/255.# Convert training and test labels to one hot matricesY_train = convert_to_one_hot(Y_train_orig, 6).TY_test = convert_to_one_hot(Y_test_orig, 6).Tprint ("number of training examples = " + str(X_train.shape[0]))print ("number of test examples = " + str(X_test.shape[0]))print ("X_train shape: " + str(X_train.shape))print ("Y_train shape: " + str(Y_train.shape))print ("X_test shape: " + str(X_test.shape))print ("Y_test shape: " + str(Y_test.shape))

model.fit(X_train, Y_train, epochs = 2, batch_size = 32)

preds = model.evaluate(X_test, Y_test)print ("Loss = " + str(preds[0]))print ("Test Accuracy = " + str(preds[1]))

120/120 [==============================] - 8s
Loss = 2.09648740292
Test Accuracy = 0.166666666667

model = load_model('ResNet50.h5') preds = model.evaluate(X_test, Y_test)print ("Loss = " + str(preds[0]))print ("Test Accuracy = " + str(preds[1]))

120/120 [==============================] - 9s
Loss = 0.530178320408
Test Accuracy = 0.866666662693

4 - Test on your own image (Optional/Ungraded)

img_path = 'images/my_image.jpg'img = image.load_img(img_path, target_size=(64, 64))x = image.img_to_array(img)x = np.expand_dims(x, axis=0)x = preprocess_input(x)print('Input image shape:', x.shape)my_image = scipy.misc.imread(img_path)imshow(my_image)print("class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ")print(model.predict(x))

Input image shape: (1, 64, 64, 3)
class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = [[ 1. 0. 0. 0. 0. 0.]]