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model.py
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model.py
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# model.py
import tensorflow as tf
def generate_model(input_shape):
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), padding='same', input_shape=input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(32, (3, 3)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(64, (3, 3), padding='same'),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(64, (3, 3)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax')
])
return model
def compile_model(model, learning_rate=0.001):
opt = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
return model