【TF2.0】From_Residual_Networks_v2a TF2.0

将Coursera 上吴恩达的教程《Convolutional Neural Networks》第2周的练习2代码转成TF2.0

文章图片

import tensorflow as tf from matplotlib.pyplot import imshow from tensorflow.keras.utils import plot_model from kt_utils import * from tensorflow.keras.preprocessing import image

def identity_block(X, f, filters, stage, block): """ Implementation of the identity block as defined in Figure 4Arguments: 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 networkReturns: 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 = tf.keras.layers.Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) X = tf.keras.layers.Activation('relu')(X)# Second component of main path (≈3 lines) X = tf.keras.layers.Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = bn_name_base + '2b')(X) X = tf.keras.layers.Activation('relu')(X)# Third component of main path (≈2 lines) X = tf.keras.layers.Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.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 = tf.keras.layers.Add()([X_shortcut, X]) X = tf.keras.layers.Activation('relu')(X)return X

np.random.seed(1) X = np.random.randn(3, 4, 4, 6).astype(np.float32) A = identity_block(X, f = 2, filters = [2, 4, 6], stage = 1, block = 'a') out = A.numpy() print(type(out)) print(out.shape) print(A[1][1][0])

文章图片

def convolutional_block(X, f, filters, stage, block, s = 2): """ Implementation of the convolutional block as defined in Figure 4Arguments: 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 usedReturns: 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 = tf.keras.layers.Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = bn_name_base + '2a')(X) X = tf.keras.layers.Activation('relu')(X)# Second component of main path (≈3 lines) X = tf.keras.layers.Conv2D(F2, (f, f), strides = (1,1), name = conv_name_base + '2b', padding = 'same', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = bn_name_base + '2b')(X) X = tf.keras.layers.Activation('relu')(X)# Third component of main path (≈2 lines) X = tf.keras.layers.Conv2D(F3, (1, 1), strides = (1,1), name = conv_name_base + '2c', padding = 'valid', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)##### SHORTCUT PATH #### (≈2 lines) X_shortcut = tf.keras.layers.Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '1', padding = 'valid', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X_shortcut) X_shortcut = tf.keras.layers.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 = tf.keras.layers.Add()([X,X_shortcut]) X = tf.keras.layers.Activation('relu')(X)return X

X = np.random.randn(3, 4, 4, 6).astype(np.float32) A = convolutional_block(X, f = 2, filters = [2, 4, 6], stage = 1, block = 'a') out = A print("out = " + str(out[1][1][0]))

文章图片

def 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 -> TOPLAYERArguments: input_shape -- shape of the images of the dataset classes -- integer, number of classesReturns: model -- a Model() instance in Keras """# Define the input as a tensor with shape input_shape X_input = tf.keras.layers.Input(input_shape)# Zero-Padding X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)# Stage 1 X = tf.keras.layers.Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.BatchNormalization(axis = 3, name = 'bn_conv1')(X) X = tf.keras.layers.Activation('relu')(X) X = tf.keras.layers.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, f = 3, filters = [128, 128, 512], stage=3, block='b') X = identity_block(X, f = 3, filters = [128, 128, 512], stage=3, block='c') X = identity_block(X, f = 3, filters = [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, f = 3, filters = [256, 256, 1024], stage=4, block='b') X = identity_block(X, f = 3, filters = [256, 256, 1024], stage=4, block='c') X = identity_block(X, f = 3, filters = [256, 256, 1024], stage=4, block='d') X = identity_block(X, f = 3, filters = [256, 256, 1024], stage=4, block='e') X = identity_block(X, f = 3, filters = [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, f = 3, filters = [512, 512, 2048], stage=5, block='b') X = identity_block(X, f = 3, filters = [512, 512, 2048], stage=5, block='c')# AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)" X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding="valid")(X)### END CODE HERE #### output layer X = tf.keras.layers.Flatten()(X) X = tf.keras.layers.Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = tf.keras.initializers.glorot_uniform(seed=0))(X)# Create model model = tf.keras.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'])

# 辅助函数

import h5pydef load_dataset(): train_dataset = h5py.File('datasets/train_signs.h5', "r") train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labelstest_dataset = h5py.File('datasets/test_signs.h5', "r") test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labelsclasses = np.array(test_dataset["list_classes"][:]) # the list of classestrain_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0])) test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classesdef convert_to_one_hot(Y, C): Y = np.eye(C)[Y.reshape(-1)].T return Y

# 导入训练数据

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Normalize image vectors X_train = X_train_orig/255. X_test = X_test_orig/255.# Convert training and test labels to one hot matrices Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_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))

# 开始训练模型

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

#导入预训练好的模型：

# load the pretrain model: model = tf.keras.models.load_model('ResNet50.h5')

# 用模型进行预测

import scipy img_path = 'images/my_image.jpg' img = tf.keras.preprocessing.image.load_img(img_path, target_size=(64, 64)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = x/255.0 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))