I have a TensorFlow model like this-
I like to know the values of the red marked layer (5 float values) for the specific input to check how the model responds at this layer (attention layer). I need this value so that I can know if my attention layer is extracting values correctly or not.
As the model is an end-to-end model, I am unsure how I can extract values of an internal layer for specific input. Can anyone please help?
You can write class Callback then pass your input and check output of each layer that you want:
import tensorflow as tf
import numpy as np
class CustomCallback(tf.keras.callbacks.Callback):
def __init__(self):
self.data = np.random.rand(1,10)
def on_epoch_end(self, epoch, logs=None):
dns_layer = self.model.layers[6]
outputs = dns_layer(self.data)
tf.print(f'\n input: {self.data}')
tf.print(f'\n output: {outputs}')
x_train = tf.random.normal((10, 32, 32))
y_train = tf.random.uniform((10, 1), maxval=10)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTM(256, input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(256))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(5, activation='softmax'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss = tf.keras.losses.SparseCategoricalCrossentropy(False))
model.summary()
for layer in model.layers:
print(layer)
model.fit(x_train, y_train , epochs=3, callbacks=[CustomCallback()], batch_size=32)
Output:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 32, 256) 295936
dropout (Dropout) (None, 32, 256) 0
lstm_1 (LSTM) (None, 256) 525312
dropout_1 (Dropout) (None, 256) 0
dense (Dense) (None, 10) 2570
dropout_2 (Dropout) (None, 10) 0
dense_1 (Dense) (None, 5) 55
dropout_3 (Dropout) (None, 5) 0
dense_2 (Dense) (None, 10) 60
=================================================================
Total params: 823,933
Trainable params: 823,933
Non-trainable params: 0
_________________________________________________________________
<keras.layers.recurrent_v2.LSTM object at 0x7f6e2163dbd0>
<keras.layers.core.dropout.Dropout object at 0x7f6da1d2efd0>
<keras.layers.recurrent_v2.LSTM object at 0x7f6d9dfe0a50>
<keras.layers.core.dropout.Dropout object at 0x7f6d9de1ec90>
<keras.layers.core.dense.Dense object at 0x7f6d9de04dd0>
<keras.layers.core.dropout.Dropout object at 0x7f6d9dd549d0>
<keras.layers.core.dense.Dense object at 0x7f6d9dd8ec90>
<keras.layers.core.dropout.Dropout object at 0x7f6d9dedd650>
<keras.layers.core.dense.Dense object at 0x7f6d9ddc2ed0>
Epoch 1/3
1/1 [==============================] - ETA: 0s - loss: 2.4188
input: [[0.91498145 0.98430978 0.22720893 0.76032816 0.78405846 0.72664182
0.7772921 0.9851892 0.41715033 0.21014543]]
output: [[0.5767021 0.04140956 0.1909151 0.06737834 0.12359484]]
1/1 [==============================] - 12s 12s/step - loss: 2.4188
Epoch 2/3
1/1 [==============================] - ETA: 0s - loss: 2.4111
input: [[0.91498145 0.98430978 0.22720893 0.76032816 0.78405846 0.72664182
0.7772921 0.9851892 0.41715033 0.21014543]]
output: [[0.5780218 0.04101932 0.18909878 0.06769065 0.12416941]]
1/1 [==============================] - 0s 376ms/step - loss: 2.4111
Epoch 3/3
1/1 [==============================] - ETA: 0s - loss: 2.3978
input: [[0.91498145 0.98430978 0.22720893 0.76032816 0.78405846 0.72664182
0.7772921 0.9851892 0.41715033 0.21014543]]
output: [[0.579072 0.04067017 0.1874026 0.0679936 0.12486164]]
1/1 [==============================] - 0s 458ms/step - loss: 2.3978