import tensorflow as tf import os import numpy as np from matplotlib import pyplot as plt from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense, GlobalAveragePooling2D from tensorflow.keras import Model np.set_printoptions(threshold=np.inf) cifar10 = tf.keras.datasets.cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 class ConvBNRelu(Model): def __init__(self, ch, kernelsz=3, strides=1, padding='same'): super(ConvBNRelu, self).__init__() self.model = tf.keras.models.Sequential([ Conv2D(ch, kernelsz, strides=strides, padding=padding), BatchNormalization(), Activation('relu') ]) def call(self, x): x = self.model(x, training=False) #在training=False时，BN通过整个训练集计算均值、方差去做批归一化，training=True时，通过当前batch的均值、方差去做批归一化。推理时 training=False效果好 return x class InceptionBlk(Model): def __init__(self, ch, strides=1): super(InceptionBlk, self).__init__() self.ch = ch self.strides = strides self.c1 = ConvBNRelu(ch, kernelsz=1, strides=strides) self.c2_1 = ConvBNRelu(ch, kernelsz=1, strides=strides) self.c2_2 = ConvBNRelu(ch, kernelsz=3, strides=1) self.c3_1 = ConvBNRelu(ch, kernelsz=1, strides=strides) self.c3_2 = ConvBNRelu(ch, kernelsz=5, strides=1) self.p4_1 = MaxPool2D(3, strides=1, padding='same') self.c4_2 = ConvBNRelu(ch, kernelsz=1, strides=strides) def call(self, x): x1 = self.c1(x) x2_1 = self.c2_1(x) x2_2 = self.c2_2(x2_1) x3_1 = self.c3_1(x) x3_2 = self.c3_2(x3_1) x4_1 = self.p4_1(x) x4_2 = self.c4_2(x4_1) # concat along axis=channel x = tf.concat([x1, x2_2, x3_2, x4_2], axis=3) return x class Inception10(Model): def __init__(self, num_blocks, num_classes, init_ch=16, **kwargs): super(Inception10, self).__init__(**kwargs) self.in_channels = init_ch self.out_channels = init_ch self.num_blocks = num_blocks self.init_ch = init_ch self.c1 = ConvBNRelu(init_ch) self.blocks = tf.keras.models.Sequential() for block_id in range(num_blocks): for layer_id in range(2): if layer_id == 0: block = InceptionBlk(self.out_channels, strides=2) else: block = InceptionBlk(self.out_channels, strides=1) self.blocks.add(block) # enlarger out_channels per block self.out_channels *= 2 self.p1 = GlobalAveragePooling2D() self.f1 = Dense(num_classes, activation='softmax') def call(self, x): x = self.c1(x) x = self.blocks(x) x = self.p1(x) y = self.f1(x) return y model = Inception10(num_blocks=2, num_classes=10) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['sparse_categorical_accuracy']) checkpoint_save_path = "./checkpoint/Inception10.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('-------------load the model-----------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True, save_best_only=True) history = model.fit(x_train, y_train, batch_size=1024, epochs=5, validation_data=(x_test, y_test), validation_freq=1, callbacks=[cp_callback]) model.summary() # print(model.trainable_variables) file = open('./weights.txt', 'w') for v in model.trainable_variables: file.write(str(v.name) + ' ') file.write(str(v.shape) + ' ') file.write(str(v.numpy()) + ' ') file.close() ############################################### show ############################################### # 显示训练集和验证集的acc和loss曲线 acc = history.history['sparse_categorical_accuracy'] val_acc = history.history['val_sparse_categorical_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] plt.subplot(1, 2, 1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, label='Validation Accuracy') plt.title('Training and Validation Accuracy') plt.legend() plt.subplot(1, 2, 2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.title('Training and Validation Loss') plt.legend() plt.show()
注意InceptionNet网络和其他网络思想