## Issue

Example below works in 2.2; `K.function`

is changed significantly in 2.3, now building a `Model`

in Eager execution, so we’re passing `Model(inputs=[learning_phase,...])`

.

I do have a workaround in mind, but it’s hackish, and lot more complex than `K.function`

; if none can show a simple approach, I’ll post mine.

```
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.python.keras import backend as K
import numpy as np
ipt = Input((16,))
x = Dense(16)(ipt)
out = Dense(16)(x)
model = Model(ipt, out)
model.compile('sgd', 'mse')
outs_fn = K.function([model.input, K.symbolic_learning_phase()],
[model.layers[1].output]) # error
x = np.random.randn(32, 16)
print(outs_fn([x, True]))
```

```
>>> ValueError: Input tensors to a Functional must come from `tf.keras.Input`.
Received: Tensor("keras_learning_phase:0", shape=(), dtype=bool)
(missing previous layer metadata).
```

## Solution

For fetching output of an intermediate layer in eager mode it’s not necessary to build a `K.function`

and use learning phase. Instead, we can build a model to achieve that:

```
partial_model = Model(model.inputs, model.layers[1].output)
x = np.random.rand(...)
output_train = partial_model([x], training=True) # runs the model in training mode
output_test = partial_model([x], training=False) # runs the model in test mode
```

Alternatively, if you insist on using `K.function`

and want to toggle learning phase in eager mode, you can use `eager_learning_phase_scope`

from `tensorflow.python.keras.backend`

(note that this module is a superset of `tensorflow.keras.backend`

and contains internal functions, such as the mentioned one, which may change in future versions):

```
from tensorflow.python.keras.backend import eager_learning_phase_scope
fn = K.function([model.input], [model.layers[1].output])
# run in test mode, i.e. 0 means test
with eager_learning_phase_scope(value=0):
output_test = fn([x])
# run in training mode, i.e. 1 means training
with eager_learning_phase_scope(value=1):
output_train = fn([x])
```

Answered By – today

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