### How to get loss gradient wrt internal layer output in tensorflow 2?

Written by- Sharif
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In TensorFlow 2, you can use the tf.GradientTape context manager to compute the gradient of a loss with respect to the output of an internal layer. Here's an example of how you can do this:

import tensorflow as tfThis code will compute the gradient of the loss with respect to the output of the internal layer (i.e., the output of the first dense layer) and store it in the gradients variable. You can then use this gradient to update the model weights using an optimizer, as shown in the last line of the code snippet.

# Build the model

model = tf.keras.Sequential([

tf.keras.layers.Dense(10, input_shape=(input_shape,), activation='relu'),

tf.keras.layers.Dense(1)

])

# Compile the model with a loss function and an optimizer

model.compile(loss='mean_squared_error', optimizer='adam')

# Generate some fake data for training

x_train = np.random.random((100, input_shape))

y_train = np.random.random((100, 1))

# Use the model to predict on the training data

with tf.GradientTape() as tape:

logits = model(x_train, training=True)

loss_value = tf.reduce_mean(tf.square(logits - y_train))

# Use the tape to compute the gradient of the loss with respect to the output of the internal layer

gradients = tape.gradient(loss_value, logits)

# Now you can use the gradients to update the model weights

optimizer.apply_gradients(zip(gradients, model.trainable_variables))