Merging tensors based on a key - tensorflow

Question

I am dealing with a problem in which network design is such that it requires merging output of one part of the network with a tabular input(other input) data based on a key and training the network further with the merged data. It appeared that there is no way two tensors can be merged based on a key. Hence though of converting tensor to numpy to pandas data and them merging. The merged data would be converted back to tensor and used further in the network. Below is the code for it:
def build_convnet(shape=(112, 112, 1)):
from keras.layers import Conv2D, BatchNormalization, MaxPool2D, GlobalMaxPool2D
momentum = .9
model = keras.Sequential()
model.add(Conv2D(64, (3,3), input_shape=shape,
padding='same', activation='relu'))
model.add(Conv2D(64, (3,3), padding='same', activation='relu'))
model.add(BatchNormalization(momentum=momentum))
model.add(MaxPool2D())
model.add(Conv2D(128, (3,3), padding='same', activation='relu'))
model.add(Conv2D(128, (3,3), padding='same', activation='relu'))
model.add(BatchNormalization(momentum=momentum))
model.add(MaxPool2D())
model.add(Conv2D(256, (3,3), padding='same', activation='relu'))
model.add(Conv2D(256, (3,3), padding='same', activation='relu'))
model.add(BatchNormalization(momentum=momentum))
model.add(MaxPool2D())
model.add(Conv2D(512, (3,3), padding='same', activation='relu'))
model.add(Conv2D(512, (3,3), padding='same', activation='relu'))
model.add(BatchNormalization(momentum=momentum))
# flatten...
model.add(GlobalMaxPool2D())
return model
def action_model(shape=(3, 112, 112, 1)):
from keras.layers import TimeDistributed, GRU, Dense, Dropout, Concatenate
# Create our convnet with (224, 224, 3) input shape
convnet = build_convnet(shape[1:])
# then create our final model
model = keras.Sequential()
# add the convnet with (5, 224, 224, 3) shape
model.add(TimeDistributed(convnet, input_shape=shape))
# here, you can also use GRU or LSTM
model.add(GRU(64))
# and finally, we make a decision network
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(64, activation='relu'))
model.add(Dense(4, activation='relu'))
return model
# create the tab_data and cnn_gru models
tab_dt = keras.Input(shape=(trainX.shape[1],))
cnn_gru = action_model(X_train.shape[1:])
# converting tensor to numpy array and merging with a tabular data on a key(Patient)
cnn_gru_np = cnn_gru.output.eval()
cnn_gru_pd = pd.Dataframe(cnn_gru_np, names = ["V1", "V2", "V3", "V4"])
cnn_gru_pd["Patient"] = train_p
tab_dt_np = tab_dt.eval()
tab_dt_pd = pd.Dataframe(tab_dt_np, names = ["Weeks", "Percent", "Age", "Sex_Male", "SmokingStatus_Ex-smoker", "SmokingStatus_Never smoked"])
tab_dt_pd["Patient"] = train_p.numpy()
combinedInput_pd = pd.merge(tab_dt_pd, cnn_gru_pd, on = ["Patient"], how = "left")
combinedInput_pd.drop(["Patient"], axis = 1, inplace = True)
combinedInput_np = np.array(combinedInput_pd)
combinedInput = tf.convert_to_tensor(combinedInput_np)
# being our regression head
x = Dense(8, activation="relu")(combinedInput)
x = Dense(1, activation="relu")(x)
model = Model(inputs=[tab_dt, cnn_gru.input], outputs=x)
I am getting the below error for eval function in the line "cnn_gru_np = cnn_gru.output.eval()"
ValueError: Cannot evaluate tensor u`enter code here`sing `eval()`: No default session is registered. Use `with sess.as_default()` or pass an explicit session to `eval(session=sess)`
Please help with suggesting what is going wrong here.

Answer 1

The reason you're getting a ValueError is that the output of a keras model isn't an eager tensor, and thus does not support eval like that.
Just try
some_model = keras.Sequential([keras.layers.Dense(10, input_shape=(5,))])
print(type(some_model.output))
print(type(tf.zeros((2,))))
some_model.output.eval()
# <class 'tensorflow.python.framework.ops.Tensor'>
# <class 'tensorflow.python.framework.ops.EagerTensor'>
# ValueError
However, there is a bigger problem with your approach: there is no connected computation graph from your models inputs to your models outputs because none of the pandas stuff are tensorflow ops. I.E. even if you were able to use eager tensors, you still wouldn't be able to train your model with automatic differentiation.
You're going to have to specify your entire model in tf I'm afraid.
Maybe you could do the data processing before giving it as input to the model? Then you only need split concat ops to put everything together?