Graph disconnected: cannot obtain value for tensor Tensor Input Keras Python

Question

I have this code:

# Declare the layers
inp1 = Input(shape=input_shape, name="input1")
inp2 = Input(shape=input_shape, name="input2")


# 128 -> 64
conv1_inp1 = Conv2D(start_neurons * 1, 3, activation="relu", padding="same")(inp1)
conv1_inp2 = Conv2D(start_neurons * 1, 3, activation="relu", padding="same")(inp2)
conv1 = Concatenate()([conv1_inp1, conv1_inp2])
conv1 = Conv2D(start_neurons * 1, 3, activation="relu", padding="same")(conv1)
conv1 = MaxPooling2D((2, 2))(conv1)
conv1 = Dropout(0.25)(conv1)

# 64 -> 32
conv2 = Conv2D(start_neurons * 2, (3, 3), activation="relu", padding="same")(conv1)
conv2 = Conv2D(start_neurons * 2, (3, 3), activation="relu", padding="same")(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)
pool2 = Dropout(0.5)(pool2)

# 32 -> 16
conv3 = Conv2D(start_neurons * 4, (3, 3), activation="relu", padding="same")(pool2)
conv3 = Conv2D(start_neurons * 4, (3, 3), activation="relu", padding="same")(conv3)
pool3 = MaxPooling2D((2, 2))(conv3)
pool3 = Dropout(0.5)(pool3)

# 16 -> 8
conv4 = Conv2D(start_neurons * 8, (3, 3), activation="relu", padding="same")(pool3)
conv4 = Conv2D(start_neurons * 8, (3, 3), activation="relu", padding="same")(conv4)
pool4 = MaxPooling2D((2, 2))(conv4)
pool4 = Dropout(0.5)(pool4)

# Middle
convm = Conv2D(start_neurons * 16, (3, 3), activation="relu", padding="same")(pool4)
convm = Conv2D(start_neurons * 16, (3, 3), activation="relu", padding="same")(convm)

# 8 -> 16
deconv4 = Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding="same")(convm)
uconv4 = Concatenate()([deconv4, conv4])
uconv4 = Dropout(0.5)(uconv4)
uconv4 = Conv2D(start_neurons * 8, (3, 3), activation="relu", padding="same")(uconv4)
uconv4 = Conv2D(start_neurons * 8, (3, 3), activation="relu", padding="same")(uconv4)

# 16 -> 32
deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="same")(uconv4)
uconv3 = Concatenate()([deconv3, conv3])
uconv3 = Dropout(0.5)(uconv3)
uconv3 = Conv2D(start_neurons * 4, (3, 3), activation="relu", padding="same")(uconv3)
uconv3 = Conv2D(start_neurons * 4, (3, 3), activation="relu", padding="same")(uconv3)

# 32 -> 64
deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv3)
uconv2 = Conv2D(start_neurons * 2, (3, 3), activation="relu", padding="same")(uconv2)
uconv2 = Conv2D(start_neurons * 2, (3, 3), activation="relu", padding="same")(uconv2)

# 64 -> 128
deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="same")(uconv2)
uconv1 = Conv2D(start_neurons * 1, (3, 3), activation="relu", padding="same")(deconv1)
uconv1 = Conv2D(start_neurons * 1, (3, 3), activation="relu", padding="same")(uconv1)

uncov1 = Dropout(0.5)(uconv1)
output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(uconv1)



# Declare the model and add the layers
model = Model(inputs = [inp1, inp2], outputs = output_layer)

model.summary()
model.compile(optimizer='adam',loss='binary_crossentropy')

And it generates this error :

Graph disconnected: cannot obtain value for tensor Tensor("input_28:0", shape=(?, 128, 128, 1), dtype=float32) at layer "input_28". The following previous layers were accessed without issue: []

The inputs have the same shape and in some forums, they say that the problem comes from the fact that the inputs are coming from 2 different sources therefore breaking the link that you had before.

I don't really know how to fix that.

Can anyone help me?

Thanks in advance.

Answer 1

This is where your graph is disconnected (uconv2 is not defined when you are calling it):

# 32 -> 64
deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv3)
uconv2 = Conv2D(start_neurons * 2, (3, 3), activation="relu", padding="same")(uconv2)

Answer 2

What fixed this graph error for me was changing this:

x_in = Input(shape=(10,), name="InputLayer")
_ = order2_embs_model(x_in)
...
model = Model(inputs=x_in, outputs=Y, name='DeepFFM') 

to this:

model = Model(inputs=order2_embs_model.inputs, outputs=Y, name='DeepFFM')