CODEWITHSUNDEEP
pythontensorflowkeras
MasterJEET
MasterJEET

Reputation: 349

"Layer is not connected, no input to return" error while trying to get intermediate layer prediction using tensorflow custom callback

I'm trying to access predictions of intermediate layers of a model during training using custom callback. Following stripped down version of the actual code demonstrates the issue.

import tensorflow as tf
import numpy as np

class Model(tf.keras.Model):
    def __init__(self, input_shape=None, name="cus_model", **kwargs):
        super(Model, self).__init__(name=name, **kwargs)
        
    def build(self, input_shape):
        self.dense1 = tf.keras.layers.Dense(input_shape=input_shape, units=32)
        
    def call(self, input_tensor):
        return self.dense1(input_tensor)

class CustomCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        get_output = tf.keras.backend.function(
            inputs = self.model.layers[0].input,
            outputs = self.model.layers[0].output
        )
        print("Layer output: ",get_output.outputs)

X = np.ones((8,16))
y = np.sum(X, axis=1)

model = Model()
model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
model.fit(X,y, epochs=8, callbacks=[CustomCallback()])

The callback is written as suggested in this answer. Getting following error:

<ipython-input-3-635fd53dbffc> in on_epoch_end(self, epoch, logs)
     12     def on_epoch_end(self, epoch, logs=None):
     13         get_output = tf.keras.backend.function(
---> 14             inputs = self.model.layers[0].input,
     15             outputs = self.model.layers[0].output
     16         )
.
.
AttributeError: Layer dense is not connected, no input to return.

What's causing this? How to resolve it?

Upvotes: 8

Views: 5621

Answers (3)

MeadowMuffins
MeadowMuffins

Reputation: 527

I guess there is some difference between Functional API and subclassing tf.keras.Model. Rewriting Dense would be also necessary. At least, it is now working. Anyone who knows why please explain.

import tensorflow as tf
import numpy as np

class Model(tf.keras.Model):
    def __init__(self, inputs=None, name="cus_model", **kwargs):
        super(Model, self).__init__(name=name, **kwargs)
        self.inputs= inputs
        self.output_dim = 8

    def build(self, input_shape):
        #tf.keras.layers.Dense(input_shape=input_shape, units=32)
        self.dense1 = self.add_weight(name='weight_vector', shape=(input_shape[1], self.output_dim),
                                      initializer='glorot_uniform', trainable=True,
                                      regularizer=None)

    def call(self, input_tensor):
        return tf.matmul(self.dense1, input_tensor)

class CustomCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        get_output = tf.keras.backend.function(
            inputs = self.model.layers[0].input,
            outputs = self.model.layers[0].output
        )
        print("Layer output: ",get_output.outputs)

X = np.ones((10,8,16))
y = np.sum(X, axis=1)

print(X.shape,y.shape)

inp = tf.keras.layers.Input((8, 16))
model = Model(inp)
# model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
# model.fit(X,y, epochs=8, callbacks=[CustomCallback()])

optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=False)

for i in range(X.shape[0]):
    with tf.GradientTape() as tape:
        out = model(X[i, :])
        label = tf.cast(y[i], dtype=tf.float32)
        loss = loss_fn(label, out)
        grads = tape.gradient(loss, model.trainable_weights)
        optimizer.apply_gradients(zip(grads, model.trainable_weights))
        print("step: ", i, "loss:", loss.numpy())

Upvotes: 0

Crystina
Crystina

Reputation: 1230

I also cannot get the self.layers[0].input because of the same error, but maybe u can directly call function defined in Model like this:

class Model(tf.keras.Model):
    def __init__(self, input_shape=None, name="cus_model", **kwargs):
        super(Model, self).__init__(name=name, **kwargs)
        if not input_shape:
            input_shape = (10,)
        self.dense1 = tf.keras.layers.Dense(input_shape=input_shape, units=32)
        self.dev_dataset = np.ones((8,16))

    def call(self, input_tensor):
        return self.dense1(input_tensor)


class CustomCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        self.model.call(self.model.dev_dataset)


X = np.ones((8,16))
y = np.sum(X, axis=1)

model = Model()
model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
model.fit(X,y, epochs=1, callbacks=[CustomCallback()])

Upvotes: 2

Marco Cerliani
Marco Cerliani

Reputation: 22031

I have no problem running this:

import tensorflow as tf
import numpy as np

X = np.ones((8,16))
y = np.sum(X, axis=1)


class CustomCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        get_output = tf.keras.backend.function(
            inputs = self.model.layers[0].input,
            outputs = self.model.layers[1].output # return output of first dense
        )
        print("\nLayer output: ", get_output(X))


inp = tf.keras.layers.Input((16,))
dense1 = tf.keras.layers.Dense(units=32)(inp)
dense2 = tf.keras.layers.Dense(units=20)(dense1)
model = tf.keras.Model(inp, dense2)

model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
model.fit(X,y, epochs=8, callbacks=[CustomCallback()])

Upvotes: 1

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