DeepLearning.ai-Week2-Residual Networks

import numpy as npfrom keras import layersfrom keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2Dfrom keras.models import Model, load_modelfrom keras.preprocessing import imagefrom keras.utils import layer_utilsfrom keras.utils.data_utils import get_filefrom keras.applications.imagenet_utils import preprocess_inputimport pydotfrom IPython.display import SVGfrom keras.utils.vis_utils import model_to_dotfrom keras.utils import plot_modelfrom resnets_utils import *from keras.initializers import glorot_uniformimport scipy.miscfrom matplotlib.pyplot import imshow%matplotlib inlineimport keras.backend as KK.set_image_data_format(‘channels_last‘)K.set_learning_phase(1)

# GRADED FUNCTION: identity_blockdef 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

Result:out = [ 0.94822997 ?0. ?????????1.16101444 ?2.747859 ???0. ?????????1.36677003]

# 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), 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(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), padding="valid", 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), padding="valid", 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]))

Result:out = [ 0.09018463 ?1.23489785 ?0.46822023 ?0.03671762 ?0. ?????????0.65516603]

# 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), name="avg_pool")(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))

Result:number of training examples = 1080number of test examples = 120X_train shape: (1080, 64, 64, 3)Y_train shape: (1080, 6)X_test shape: (120, 64, 64, 3)Y_test shape: (120, 6)

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

Result:

Epoch 1/21080/1080 [==============================] - 245s 227ms/step - loss: 3.0501 - acc: 0.2611Epoch 2/21080/1080 [==============================] - 240s 223ms/step - loss: 2.3643 - acc: 0.3185

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

Result:
120/120 [==============================] - 8s 68ms/step
Loss = 13.4317462285
Test Accuracy = 0.166666667163

model = load_model(‘ResNet50.h5‘) 

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

Result: 

120/120 [==============================] - 17s 142ms/stepLoss = 0.530178316434Test Accuracy = 0.866666662693

model.summary()

Result:
（略）

plot_model(model, to_file=‘model.png‘)SVG(model_to_dot(model).create(prog=‘dot‘, format=‘svg‘))