Confusion about keras Model: __call__ vs. call vs. predict methods

问题

I have realized that I do not quite understand the difference between calling either the __call__, call, or predict method of a Keras' model.

For example, we have a trained keras model. After calling the code:

# After training.
y_pred_1 = model(X_new)
y_pred_2 = model.call(X_new)
y_pred_3 = model.predict(X_new)

I expected that y_pred_1, y_pred_2, and y_pred_3 are all the same. But it turned out that they are not the same.

Could you please explain to me the difference?

回答1:

Just to complement the answer as I was also searching for this. When you need to specify the training flag of the model for the inference phase, such as, model(X_new, training=False) when you have a batch normalization layer, for example, both predict and predict_on_batch already do that when they are executed.

So, model(X_new, training=False) and model.predict_on_batch(X_new) are equivalent.

The difference between predict and predict_on_batch is that the latter runs over a single batch, and the former runs over a dataset that is splitted into batches and the results merged to produce the final numpy array of predictions.

Beyond the difference mentioned by @Dmitry Kabanov, the functions generate different types of output, __call__ generates a Tensor, and predict and predict_on_batch generate numpy.ndarray, and according to the documentation, __call__ is faster than the predict function for small scale inputs, i.e., which fit in one batch.

回答2:

My bad, it was a mistake in my code.

It turned out that there is no essential difference between these three methods.

The only difference is that call accepts only tensors, while the other two methods also accept NumPy arrays.

Here is a toy code showing that three methods are the same:

import numpy as np
import tensorflow as tf


model = tf.keras.Sequential(
    [
        tf.keras.layers.InputLayer(input_shape=(2, )),
        tf.keras.layers.Dense(2),
    ]
)
model.compile(loss='mse')

W = model.trainable_variables[0]
W.assign(np.array([[1.0, 0.0], [0.0, 1.0]]).T)

input = np.array([[1.0, 2.0], [3.0, 4.0], ], dtype=np.float32)

print("__call__:")
print(model(input))

print("Call:")
print(model.call(tf.convert_to_tensor(input)))

print("Predict:")
print(model.predict(input))

来源：https://stackoverflow.com/questions/60837962/confusion-about-keras-model-call-vs-call-vs-predict-methods

标签

tensorflow

keras