ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 223461, 5), found shape=(None, 5)

Question

I use a model with a combinaison of GRu and Conv1D. When I want to fit the model I get an error in:

ValueError: Input 0 of layer "sequential_8" is incompatible with the layer: expected shape=(None, 223461, 5), found shape=(None, 5)

The shape of X_train is (223461, 5), whereas the y_train is (223461,)

This is my code:

verbose, epochs, batch_size = 0, 100, 64
n_timesteps, n_features, n_outputs = X_train.shape[0], X_train.shape[1], y_train.shape[0]
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(GRU(64))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(n_outputs, activation='softmax'))
opt = Adam(learning_rate=0.01)
model.compile(loss='categorical_crossentropy', optimizer=opt , metrics=['accuracy'])
model.summary()

The output of summary is:

Model: "sequential_8"
_____  Layer (type)                Output Shape              Param #
=====  conv1d_8 (Conv1D)           (None, 223459, 64)        1024
        max_pooling1d_8 (MaxPooling  (None, 111729, 64)       0           1D)

        gru_7 (GRU)                 (None, 64)                24960
        dropout_14 (Dropout)        (None, 64)                0
        flatten_6 (Flatten)         (None, 64)                0
        dense_14 (Dense)            (None, 128)               8320
        dropout_15 (Dropout)        (None, 128)               0
        dense_15 (Dense)            (None, 223461)            28826469

===== Total params: 28,860,773 Trainable params: 28,860,773 Non-trainable params: 0
_____

and here where I face the error:

model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)
_, accuracy = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)

Denis Savenko · Accepted Answer · 2022-04-12 06:21:16Z

1

According to your model, your training data x_train and y_train is just a piece of data.

So your training data have to expand the dimension, like this:

X_train = X_train[None,:]
y_train = y_train[None,:]

Or use tensorflow function to do this :

X_train = tf.expand_dims(X_train, axis=0)
y_train = tf.expand_dims(y_train, axis=0)

The output shape of the model will be (1,223461)

If the output is not what you expected, it means your model design is wrong.

edited Apr 12, 2022 at 6:21

Denis Savenko

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answered Apr 12, 2022 at 4:26

Augustus Hsu

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7 Comments

MexcelsiorB Over a year ago

This is the architecture that I want to use in my model is it right how I made it ? I want to make a binary classification of numerical data. And my classes are 0 and 1 . @AugustusHsu

Augustus Hsu Over a year ago

I think the architecture is fine. But I want to check your dataset first. Dose the shape of X_train (223461, 5) mean you have 223461 data with 5 sequence and 1 dimension? or 1 data with 223461 sequence and 5 dimension?

MexcelsiorB Over a year ago

yes yes you could check my colab here please . yes I want to mean 223461 sequence and 5 dimension . @Augustus

Augustus Hsu Over a year ago

OK. Now you have X_train, but what do you want to predict? For example: If you want to predict the next timestamp of vmcategory. You need to set the n_timesteps and modify the X_train as (batch, n_timesteps, 5) to predict the vmcategory. Please check WindowGenerator to understand what does it do.

Augustus Hsu Over a year ago

Also, I modify your code to this(I'm not sure if this is what you want.). It just is a sketch, you can change architecture, loss function or parameter(like n_timesteps).

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