How can I print the intermediate variables in the loss function in TensorFlow and Keras?

Question

I\'m writing a custom objective to train a Keras (with TensorFlow backend) model but I need to debug some intermediate computation. For simplicity, let\'s say I have:

Answer 1

This is not something that is solved in Keras as far as I know, so you have to resort to backend-specific functionality. Both Theano and TensorFlow have Print nodes that are identity nodes (i.e., they return the input node) and have the side-effect of printing the input (or some tensor of the input).

Example for Theano:

diff = y_pred - y_true
diff = theano.printing.Print('shape of diff', attrs=['shape'])(diff)
return K.square(diff)

Example for TensorFlow:

diff = y_pred - y_true
diff = tf.Print(diff, [tf.shape(diff)])
return K.square(diff)

Note that this only works for intermediate values. Keras expects tensors that are passed to other layers to have specific attributes such as _keras_shape. Values processed by the backend, i.e. through Print, usually do not have that attribute. To solve this, you can wrap debug statements in a Lambda layer for example.

Answer 2

In TensorFlow 2.0, you can use tf.print and print anything inside the definition of your loss function. You can also do something like tf.print("my_intermediate_tensor =", my_intermediate_tensor), i.e. with a message, similar to Python's print. However, you may need to decorate your loss function with @tf.function to actually see the results of the tf.print.

Answer 3

In TensorFlow 2, you can now add IDE breakpoints in the TensorFlow Keras models/layers/losses, including when using the fit, evaluate, and predict methods. However, you must add model.run_eagerly = True after calling model.compile() for the values of the tensor to be available in the debugger at the breakpoint. For example,

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

def custom_loss(y_pred, y_true):
    diff = y_pred - y_true
    return tf.keras.backend.square(diff)  # Breakpoint in IDE here. =====

class SimpleModel(Model):

    def __init__(self):
        super().__init__()
        self.dense0 = Dense(2)
        self.dense1 = Dense(1)

    def call(self, inputs):
        z = self.dense0(inputs)
        z = self.dense1(z)
        return z

x = tf.convert_to_tensor([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)
y = tf.convert_to_tensor([0, 1], dtype=tf.float32)

model0 = SimpleModel()
model0.run_eagerly = True
model0.compile(optimizer=Adam(), loss=custom_loss)
y0 = model0.fit(x, y, epochs=1)  # Values of diff *not* shown at breakpoint. =====

model1 = SimpleModel()
model1.compile(optimizer=Adam(), loss=custom_loss)
model1.run_eagerly = True
y1 = model1.fit(x, y, epochs=1)  # Values of diff shown at breakpoint. =====

This also works for debugging the outputs of intermediate network layers (for example, adding the breakpoint in the call of the SimpleModel).

Note: this was tested in TensorFlow 2.0.0-rc0.