Inception 10ネットワークpythonでのTensorFlow 2の実装

参考資料:北京大学、ソフトマイクロ学院、曹健先生、『人工知能実践:TensorFlow 2.0ノート』運行環境:python 3.7 tensorflow 2.1.0 numpy 1.17.4 matplotlib 3.2.1

# inception10
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)  # training=False, BN           、      
        return x


#   inception    
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):
        # *16
        x1 = self.c1(x)

        # *16
        x2_1 = self.c2_1(x)
        x2_2 = self.c2_2(x2_1)

        # *16
        x3_1 = self.c3_1(x)
        x3_2 = self.c3_2(x3_1)

        # *16
        x4_1 = self.p4_1(x)
        x4_2 = self.c4_2(x4_1)

        #    1*32*32*64
        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)
        # filters       block
        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)

            #   
            self.out_channels *=2
            #      1*32*32*64  16*4
            #      1*32*32*128 32*4
        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


#  10  2  ，   2   ，    4 
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=128, epochs=5, validation_data=(x_test, y_test), validation_freq=1, callbacks=[cp_callback])
model.summary()

# print(model.trainable_variables)
file = open('./weights_inception10.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   ###############################################

#           acc loss  
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()