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- convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
- convnet = conv_2d(convnet, 32, 2, activation='relu')
- convnet = max_pool_2d(convnet, 2)
- convnet = conv_2d(convnet, 64, 2, activation='relu')
- convnet = max_pool_2d(convnet, 2)
- convnet = fully_connected(convnet, 1024, activation='relu')
- convnet = dropout(convnet, 0.8)
- convnet = fully_connected(convnet, 2, activation='softmax')
- convnet = regression(convnet, optimizer='adam', learning_rate=lr, loss='categorical_crossentropy', name='targets')
- model = tflearn.DNN(convnet, tensorboard_dir='log')
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