def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5)
result = layer.max_pool(result) # 14
result = layer.drop_conv(keep_prob, result, 18, 24, width=5)
result = tf.nn.relu(result)
result = layer.max_pool(result) # 7
return layer.drop_conv(keep_prob,
result,
24,
10,
width=7,
padding="VALID")
def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5, padding="VALID")
result = layer.max_pool(result) # 12
result = layer.resnet_block(result, 18, 3, training)
result = layer.resnet_block(result, 18, 3, training)
result = layer.max_pool(result) # 6
result = layer.conv_relu(result, 18, 24, width=1)
result = layer.resnet_narrow(result, 24, 3, training)
result = layer.resnet_narrow(result, 24, 3, training)
result = layer.max_pool(result) # 3
result = layer.conv_relu(result, 24, 32, width=1)
result = layer.resnet_narrow(result, 32, 3, training)
result = layer.resnet_narrow(result, 32, 3, training)
return layer.drop_conv(keep_prob,
result,
32,
10,
width=3,
padding="VALID")