I am trying to construct a multi-channel convolutional neural network. I have two tensors (i.e., `training_x1`

and `training_x2`

), each including float values with `(476, 47, 3, 1)`

dimension. I build the multi-channel NN as follows (a minimal example):

```
import numpy as np
from tensorflow import keras
from keras.models import Model
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, concatenate, AveragePooling2D, Input
training_x1 = np.zeros((476, 47, 3, 1)) # just as an example
training_x2 = np.zeros((476, 47, 3, 1)) # just as an example
inputs1 = Input(shape=(training_x1.shape[0], ))
conv1 = Conv2D(filters=32, kernel_size=3, activation='relu')(training_x1)
drop1 = Dropout(0.5)(conv1)
pool1 = MaxPooling2D(pool_size=2)(drop1)
flat1 = Flatten()(pool1)
# channel 2
inputs2 = Input(shape=(training_x2.shape[0], ))
conv2 = Conv2D(filters=32, kernel_size=3, activation='relu')(training_x2)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling2D(pool_size=2)(drop2)
flat2 = Flatten()(pool2)
# merge
merged = concatenate([flat1, flat2])
# interpretation
dense1 = Dense(10, activation='relu')(merged)
outputs = Dense(1, activation='linear')(dense1)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
```

However, my model fails with error *ValueError: Output tensors of a Functional model must be the output of a TensorFlow Layer (thus holding past layer metadata).* What mistake am I making?