From 9a87b6f78f6dadd959633fdb8afc5bf5b24456a1 Mon Sep 17 00:00:00 2001 From: mbnacwlh3 Date: Sat, 2 Oct 2021 12:52:25 +0800 Subject: [PATCH] Delete 'DenseNet.py' --- DenseNet.py | 130 ---------------------------------------------------- 1 file changed, 130 deletions(-) delete mode 100644 DenseNet.py diff --git a/DenseNet.py b/DenseNet.py deleted file mode 100644 index 6893226..0000000 --- a/DenseNet.py +++ /dev/null @@ -1,130 +0,0 @@ -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) \ No newline at end of file