tensorlayer3/DenseNet.py

import tensorflow as tf

class BottleNeck(tf.keras.layers.Layer):
    def __init__(self, growth_rate, drop_rate):
        super(BottleNeck, self).__init__()
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.conv1 = tf.keras.layers.Conv2D(filters=4 * growth_rate,
                                            kernel_size=(1, 1),
                                            strides=1,
                                            padding="same")
        self.bn2 = tf.keras.layers.BatchNormalization()
        self.conv2 = tf.keras.layers.Conv2D(filters=growth_rate,
                                            kernel_size=(3, 3),
                                            strides=1,
                                            padding="same")
        self.dropout = tf.keras.layers.Dropout(rate=drop_rate)

        self.listLayers = [self.bn1,
                           tf.keras.layers.Activation("relu"),
                           self.conv1,
                           self.bn2,
                           tf.keras.layers.Activation("relu"),
                           self.conv2,
                           self.dropout]

    def call(self, x):
        y = x
        for layer in self.listLayers.layers:
            y = layer(y)
        y = tf.keras.layers.concatenate([x, y], axis=-1)
        return y


class DenseBlock(tf.keras.layers.Layer):
    def __init__(self, num_layers, growth_rate, drop_rate=0.5):
        super(DenseBlock, self).__init__()
        self.num_layers = num_layers
        self.growth_rate = growth_rate
        self.drop_rate = drop_rate
        self.listLayers = []
        for _ in range(num_layers):
            self.listLayers.append(BottleNeck(growth_rate=self.growth_rate, drop_rate=self.drop_rate))

    def call(self, x):
        for layer in self.listLayers.layers:
            x = layer(x)
        return x
class TransitionLayer(tf.keras.layers.Layer):
    def __init__(self, out_channels):
        super(TransitionLayer, self).__init__()
        self.bn = tf.keras.layers.BatchNormalization()
        self.conv = tf.keras.layers.Conv2D(filters=out_channels,
                                           kernel_size=(1, 1),
                                           strides=1,
                                           padding="same")
        self.pool = tf.keras.layers.MaxPool2D(pool_size=(2, 2),
                                              strides=2,
                                              padding="same")

    def call(self, inputs):
        x = self.bn(inputs)
        x = tf.keras.activations.relu(x)
        x = self.conv(x)
        x = self.pool(x)
        return x
class DenseNet(tf.keras.Model):
    def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):
        super(DenseNet, self).__init__()
        self.conv = tf.keras.layers.Conv2D(filters=num_init_features,
                                           kernel_size=(7, 7),
                                           strides=2,
                                           padding="same")
        self.bn = tf.keras.layers.BatchNormalization()
        self.pool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
                                              strides=2,
                                              padding="same")
        self.num_channels = num_init_features
        self.dense_block_1 = DenseBlock(num_layers=block_layers[0], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[0]
        self.num_channels = compression_rate * self.num_channels
        self.transition_1 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_2 = DenseBlock(num_layers=block_layers[1], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[1]
        self.num_channels = compression_rate * self.num_channels
        self.transition_2 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_3 = DenseBlock(num_layers=block_layers[2], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[2]
        self.num_channels = compression_rate * self.num_channels
        self.transition_3 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_4 = DenseBlock(num_layers=block_layers[3], growth_rate=growth_rate, drop_rate=drop_rate)

        self.avgpool = tf.keras.layers.GlobalAveragePooling2D()
        self.fc = tf.keras.layers.Dense(units=10,
                                        activation=tf.keras.activations.softmax)

    def call(self, inputs):
        x = self.conv(inputs)
        x = self.bn(x)
        x = tf.keras.activations.relu(x)
        x = self.pool(x)

        x = self.dense_block_1(x)
        x = self.transition_1(x)
        x = self.dense_block_2(x)
        x = self.transition_2(x)
        x = self.dense_block_3(x)
        x = self.transition_3(x,)
        x = self.dense_block_4(x)

        x = self.avgpool(x)
        x = self.fc(x)

        return x

def densenet():
    return DenseNet(num_init_features=64, growth_rate=32, block_layers=[4,4,4,4], compression_rate=0.5, drop_rate=0.5)
mynet=densenet()
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train.reshape((60000, 28, 28, 1)).astype('float32') / 255
x_test = x_test.reshape((10000, 28, 28, 1)).astype('float32') / 255

mynet.compile(loss='sparse_categorical_crossentropy',
              optimizer=tf.keras.optimizers.Adam(),
              metrics=['accuracy'])

history = mynet.fit(x_train, y_train,
                    batch_size=64,
                    epochs=5,
                    validation_split=0.2)
test_scores = mynet.evaluate(x_test, y_test, verbose=2)