How can I print the values of Keras tensors?

Keras' backend has print_tensor which enables you to do this. You can use it this way:

import keras.backend as Kdef loss_fn(y_true, y_pred):    y_true = K.print_tensor(y_true, message='y_true = ')    y_pred = K.print_tensor(y_pred, message='y_pred = ')    ...

The function returns an identical tensor. When that tensor is evaluated, it will print its content, preceded by message.From the Keras docs:

Note that print_tensor returns a new tensor identical to x which should be used in the following code. Otherwise the print operation is not taken into account during evaluation.

So, make sure to use the tensor afterwards.

Usually, y_true you know in advance - during preparation of your train corpora...

However, there's one trick to see the values inside y_true and/or y_pred. Keras gives you an opportunity to write respective callback for printing the neural network's output.It will look something like this:

def loss_fn(y_true, y_pred):    return y_true # or y_pred...import keras.callbacks as cbksclass CustomMetrics(cbks.Callback):    def on_epoch_end(self, epoch, logs=None):        for k in logs:            if k.endswith('loss_fn'):               print logs[k]

Here the loss_fn is name of your loss function when you pass it into the model.compile(...,metrics=[loss_fn],) function during model's compilation.

So, finally, you have to pass this CustomMetrics callback as the argument into the model.fit():

model.fit(x=train_X, y=train_Y, ... , callbacks=[CustomMetrics()])

P.S.: If you use Theano (or TensorFlow) like here in Keras, you write a python program, and then you compile it and execute. So, in your example y_true - is just a tensor variable which is used for further compilation and loss function counting.

It means that there's no way to see the values inside it. In Theano, for example, you can look inside the only so-called shared variable after the execution of respective eval() function. See this question for more info.

You could redefine your loss function to return the value instead:

def loss_fn(y_true, y_pred):    return y_true

Let's create some tensors:

from keras import backend as Ka = K.constant([1,2,3])b = K.constant([4,5,6])

And use the keras.backend.eval() API to evaluate your loss function:

loss = loss_fn(a,b)K.eval(loss)# array([1., 2., 3.], dtype=float32)