InceptionNet.py

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
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'): # 这里是定义了默认参数，如果不给就默认，strides是滑动步长
        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)
        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

# ---------------------------------------
# 上面两个class只是定义了一个结构单元
# 还要用这些结构单元堆叠形成完整的网络,
# 注意这里缺少使用Flatten？
# ---------------------------------------

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): # 这里是指1个block中有两个结构单元
                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 = tf.keras.layers.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/Baseline.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=32,epochs=2,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])
model.summary()

file = open('./Lenet.txt','w')
for v in model.trainable_variables:
    file.write(str(v.name) + '\n')
    file.write(str(v.shape) + '\n')
    file.write(str(v.numpy()) + '\n')
file.close()



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()