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I am trying to train a keras model. I have a random integer in the model, and I would like to print it every epoch to make sure it is in fact changing.

rand_int = tf.random.uniform((), 0, 2, dtype=tf.int32)
...
model.fit(X, y epochs = 10, batch_size = 20, validation_split=0.1)

How would I do this?

Akshay Sehgal
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PiccolMan
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  • How you add the integer in the model, beginning/middle/last! Can you show some example; just define a small net? – Innat Dec 04 '20 at 02:03
  • Does this answer your question? [how to see tensor value of a layer output in keras](https://stackoverflow.com/questions/56262735/how-to-see-tensor-value-of-a-layer-output-in-keras) – Innat Dec 04 '20 at 02:10
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    You can use `callbacks` to solve what you want. Do check my answer, that should give you some direction. – Akshay Sehgal Dec 04 '20 at 19:30

2 Answers2

4

You can write a custom Callback and use it each time an epoch ends.

class CustomCallback(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        rand_int = tf.random.uniform((), 0, 2, dtype=tf.int32)
        print(rand_int)
        
model.fit(X, y epochs = 10, batch_size = 20, validation_split=0.1, callbacks=[CustomCallback()])

More details here.

For example, here is a dummy code to print the weights and biases of layer[1] after each epoch. You can set up the function in a way that you prefer.

from tensorflow.keras import layers, Model, callbacks

class CustomCallback(callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        print(' ')
        print(' ')
        print(model.layers[1].get_weights())
        

X, y = np.random.random((10,5)), np.random.random((10,))

inp = layers.Input((5,))
x = layers.Dense(3)(inp)
out = layers.Dense(1)(x)

model = Model(inp, out)

model.compile(loss='MAE',metrics=['accuracy'])
model.fit(X,y,callbacks=[CustomCallback()], epochs=3)

Epoch 1/3
1/1 [==============================] - ETA: 0s - loss: 0.2346 - accuracy: 0.0000e+00 
 
[array([[ 0.16518219, -0.44628695, -0.07702655],
       [-0.1993848 ,  0.03855793, -0.62964785],
       [ 0.5592851 , -0.28281152, -0.23358124],
       [ 0.05242977,  0.4023881 , -0.19522922],
       [ 0.07936202, -0.40436065,  0.10003945]], dtype=float32), array([ 0.01530731, -0.01565045, -0.01581042], dtype=float32)]
1/1 [==============================] - 0s 2ms/step - loss: 0.2346 - accuracy: 0.0000e+00
Epoch 2/3
1/1 [==============================] - ETA: 0s - loss: 0.2337 - accuracy: 0.0000e+00 
 
[array([[ 0.16814367, -0.4492649 , -0.08000461],
       [-0.19710523,  0.03622784, -0.6319782 ],
       [ 0.55797213, -0.28144714, -0.23221655],
       [ 0.05509637,  0.3996864 , -0.19793113],
       [ 0.07731982, -0.40226308,  0.10213734]], dtype=float32), array([ 0.01846951, -0.01881272, -0.01897269], dtype=float32)]
1/1 [==============================] - 0s 7ms/step - loss: 0.2337 - accuracy: 0.0000e+00
Epoch 3/3
1/1 [==============================] - ETA: 0s - loss: 0.2322 - accuracy: 0.0000e+00 
 
[array([[ 0.16706704, -0.448164  , -0.07889817],
       [-0.19894598,  0.0381193 , -0.63007975],
       [ 0.5558067 , -0.27921563, -0.22997847],
       [ 0.05663134,  0.3981127 , -0.19951159],
       [ 0.07536169, -0.400249  ,  0.10415838]], dtype=float32), array([ 0.01846951, -0.01881272, -0.01897269], dtype=float32)]
1/1 [==============================] - 0s 2ms/step - loss: 0.2322 - accuracy: 0.0000e+00
Akshay Sehgal
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0

I guess something like the following code will do it.

for epoch in range(10):
  ...
  #Print tensor values...
  ...
  model.fit(X, y, epochs=1, ...)
krenerd
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