问题
I've a simple model and need access of intermediate layers within a custom callback to get intermediate predictions.
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
)
print("\nLayer output: ", get_output(X))
If I build the model by sub-classing like below:
class Model(tf.keras.Model):
def build(self, input_shape):
self.dense1 = tf.keras.layers.Dense(units=32)
self.dense2 = tf.keras.layers.Dense(units=1)
def call(self, input_tensor):
x = self.dense1(input_tensor)
x = self.dense2(x)
return x
model = Model()
model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
model.fit(X,y, epochs=2, callbacks=[CustomCallback()])
I get following error:
<ipython-input-8-bab75191182e> in on_epoch_end(self, epoch, logs)
2 def on_epoch_end(self, epoch, logs=None):
3 get_output = tf.keras.backend.function(
----> 4 inputs = self.model.layers[0].input,
5 outputs = self.model.layers[1].output
6 )
.
.
AttributeError: Layer dense is not connected, no input to return.
whereas if I build model using functional API like below, it works as expected.
initial = tf.keras.layers.Input((16,))
x = tf.keras.layers.Dense(units=32)(initial)
final = tf.keras.layers.Dense(units=1)(x)
model = tf.keras.Model(initial, final)
model.compile(optimizer='adam',loss='mean_squared_error', metrics='accuracy')
model.fit(X,y, epochs=2, callbacks=[CustomCallback()])
What's causing the error in case of model sub-classing?
TF version: 2.2.0
来源:https://stackoverflow.com/questions/62668398/layer-is-not-connected-issue-while-accessing-intermediate-layer-from-within-th