ValueError: Graph Disconnected: Cannot Obtain Value For Tensor Tensor...The Following Previous Layers Were Accessed Without Issue: []

I have been trying to create a multi-input model using Keras, but got errors. The idea is to combine the text and corresonding topics to make predictions for sentiments. Here's the

Solution 1:

There are few issues with your Keras functional API implementation,

1. You should use the Concatenate layer as Concatenate(axis=-1)([text_encoded, topic_input]).

2. In the concatenate layer you are trying to combine an int32 tensor and a float32 tensor, which is not allowed. What you should do is, from keras.backend import cast and concatenated = Concatenate(axis=-1)([text_encoded, cast(topic_input, 'float32')]).

3. You got variable conflicts, there are two sentiment variables, one pointing to a to_categorical output and the other the output of the final Dense layer.

4. Your model inputs cannot be intermediate tensors like text_encoded. They should come from Input layers.

To help with your implementation, here's a working version of your code (I am not sure if this is exactly what you wanted though) in TF 1.13.

from keras.utils import to_categorical
text = np.random.randint(5000, size=(442702, 200), dtype='int32')
topic = np.random.randint(2, size=(442702, 227), dtype='int32')
sentiment1 = to_categorical(np.random.randint(5, size=442702), dtype='int32')

from keras.models import Sequential
from keras.layers import Input, Dense, Activation, Embedding, Flatten, GlobalMaxPool1D, Dropout, Conv1D, Concatenate, Lambda
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.losses import binary_crossentropy
from keras.optimizers import Adam
from keras.backend import cast
from keras.models import Model

text_input = Input(shape=(200,), dtype='int32', name='text')
text_encoded = Embedding(input_dim=5000, output_dim=20, input_length=200)(text_input)
text_encoded = Dropout(0.1)(text_encoded)
text_encoded = Conv1D(300, 3, padding='valid', activation='relu', strides=1)(text_encoded)
text_encoded = GlobalMaxPool1D()(text_encoded)

topic_input = Input(shape=(227,), dtype='int32', name='topic')

topic_float = Lambda(lambda x:cast(x, 'float32'), name='Floatconverter')(topic_input)

concatenated = Concatenate(axis=-1)([text_encoded, topic_float])
sentiment = Dense(5, activation='softmax')(concatenated)

model = Model(inputs=[text_input, topic_input], outputs=sentiment)
# summarize layers
print(model.summary())

Hope these help.