What is the difference between the predict and predict_on_batch methods of a Keras model?

Question

According to the keras documentation:

predict_on_batch(self, x)
Returns predictions for a single batch of samples.

However, there does not

Answer 1

I just want to add something that does not fit in a comment. It seems that predict check carefully the output shape:

class ExtractShape(keras.engine.topology.Layer):
    def call(self, x):
        return keras.backend.sum(x, axis=0)
    def compute_output_shape(self, input_shape):
        return input_shape

a = keras.layers.Input((None, None))
b = ExtractShape()(a)
m = keras.Model(a, b)
m.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy')
A = np.ones((5,4,3))

Then:

In [163]: m.predict_on_batch(A)
Out[163]: 
array([[5., 5., 5.],
       [5., 5., 5.],
       [5., 5., 5.],
       [5., 5., 5.]], dtype=float32)
In [164]: m.predict_on_batch(A).shape
Out[164]: (4, 3)

But:

In [165]: m.predict(A)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
 in ()

----> 1 m.predict(A)

~/miniconda3/envs/ccia/lib/python3.6/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps)
   1746         f = self.predict_function
   1747         return self._predict_loop(f, ins, batch_size=batch_size,
-> 1748                                   verbose=verbose, steps=steps)
   1749 
   1750     def train_on_batch(self, x, y,

~/miniconda3/envs/ccia/lib/python3.6/site-packages/keras/engine/training.py in _predict_loop(self, f, ins, batch_size, verbose, steps)
   1306                         outs.append(np.zeros(shape, dtype=batch_out.dtype))
   1307                 for i, batch_out in enumerate(batch_outs):
-> 1308                     outs[i][batch_start:batch_end] = batch_out
   1309                 if verbose == 1:
   1310                     progbar.update(batch_end)

ValueError: could not broadcast input array from shape (4,3) into shape (5,3)

I am not sure if this is a bug really.