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Update model_tensorflow.py
Browse files- model_tensorflow.py +5 -8
model_tensorflow.py
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import tensorflow as tf
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from tensorflow.keras import layers, Model
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# Separable Convolution Block
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# ==============================
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def separable_block(x, filters, dropout_rate=0.25):
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x = layers.SeparableConv2D(filters, 3, padding="same", use_bias=False)(x)
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x = layers.BatchNormalization()(x)
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return x
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# Model Definition
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# ==============================
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def build_sara_tf_model(input_shape=(150, 150, 3), num_classes=6):
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inputs = tf.keras.Input(shape=input_shape)
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x = layers.Conv2D(32, 3, padding="same", use_bias=False)(inputs)
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x = layers.BatchNormalization()(x)
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x = layers.Activation("relu")(x)
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x = separable_block(x, 64, 0.25)
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x = separable_block(x, 128, 0.25)
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x = separable_block(x, 256, 0.30)
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x = layers.GlobalAveragePooling2D()(x)
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x = layers.Dense(128, use_bias=False)(x)
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import tensorflow as tf
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from tensorflow.keras import layers, Model
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def separable_block(x, filters, dropout_rate=0.25):
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x = layers.SeparableConv2D(filters, 3, padding="same", use_bias=False)(x)
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x = layers.BatchNormalization()(x)
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return x
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# Model Definition
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def build_sara_tf_model(input_shape=(150, 150, 3), num_classes=6):
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inputs = tf.keras.Input(shape=input_shape)
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x = layers.Conv2D(32, 3, padding="same", use_bias=False)(inputs)
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x = layers.BatchNormalization()(x)
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x = layers.Activation("relu")(x)
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x = separable_block(x, 64, 0.25)
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x = separable_block(x, 128, 0.25)
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x = separable_block(x, 256, 0.30)
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x = layers.GlobalAveragePooling2D()(x)
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x = layers.Dense(128, use_bias=False)(x)
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