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114 lines
2.9 KiB
Python
114 lines
2.9 KiB
Python
# Tensorflow v2.3.1
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"""
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Programmed by the-robot <https://github.com/the-robot>
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"""
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from tensorflow.keras.layers import (
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Conv2D,
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Dense,
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Dropout,
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Flatten,
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Input,
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Lambda,
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MaxPooling2D,
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)
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from tensorflow.keras import Model
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import tensorflow as tf
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import typing
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tf.config.run_functions_eagerly(True)
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@tf.function
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def AlexNet(input_shape: typing.Tuple[int], classes: int = 1000) -> Model:
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"""
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Implementation of the AlexNet architecture.
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Arguments:
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input_shape -- shape of the images of the dataset
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classes -- integer, number of classes
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Returns:
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model -- a Model() instance in Keras
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Note:
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when you read the paper, you will notice that the channels (filters) in the diagram is only
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half of what I have written below. That is because in the diagram, they only showed model for
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one GPU (I guess for simplicity). However, during the ILSVRC, they run the network across 2 NVIDIA GTA 580 3GB GPUs.
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Also, in paper, they used Local Response Normalization. This can also be done in Keras with Lambda layer.
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You can also use BatchNormalization layer instead.
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"""
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# convert input shape into tensor
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X_input = Input(input_shape)
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# NOTE: layer 1-5 is conv-layers
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# layer 1
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X = Conv2D(
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filters = 96,
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kernel_size = (11, 11),
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strides = (4, 4),
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activation = "relu",
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padding = "same",
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)(X_input)
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X = MaxPooling2D(pool_size = (3, 3), strides = (2, 2))(X)
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X = Lambda(tf.nn.local_response_normalization)(X)
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# layer 2
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X = Conv2D(
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filters = 256,
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kernel_size = (5, 5),
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strides = (1, 1),
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activation = "relu",
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padding = "same",
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)(X)
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X = MaxPooling2D(pool_size = (3, 3), strides = (2, 2))(X)
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X = Lambda(tf.nn.local_response_normalization)(X)
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# layer 3
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X = Conv2D(
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filters = 384,
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kernel_size = (3, 3),
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strides = (1, 1),
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activation = "relu",
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padding = "same",
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)(X)
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# layer 4
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X = Conv2D(
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filters = 384,
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kernel_size = (3, 3),
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strides = (1, 1),
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activation = "relu",
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padding = "same",
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)(X)
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# layer 5
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X = Conv2D(
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filters = 256,
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kernel_size = (3, 3),
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strides = (1, 1),
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activation = "relu",
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padding = "same",
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)(X)
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X = MaxPooling2D(pool_size = (3, 3), strides = (2, 2))(X)
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X = Lambda(tf.nn.local_response_normalization)(X)
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# NOTE: layer 6-7 is fully-connected layers
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# layer 6
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X = Flatten()(X)
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X = Dense(units = 4096, activation = 'relu')(X)
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X = Dropout(0.5)(X)
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# layer 7
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X = Dense(units = 4096, activation = 'relu')(X)
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X = Dropout(0.5)(X)
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# layer 8 (classification layer)
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# use sigmoid if binary classificaton and softmax if multiclass classification
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X = Dense(units = classes, activation = "softmax")(X)
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model = Model(inputs = X_input, outputs = X, name = "AlexNet")
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return model
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