ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 455, 30), found shape=(None, 30)

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ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 455, 30), found shape=(None, 30)

  1. How to solve ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 455, 30), found shape=(None, 30)

    The Tensorflow model expects the first dimension of the input to be the batch size, in the model declaration however they set the input shape to be the same shape as the input. To fix this you can change the input shape of the model to be the number of feature in the dataset.
    model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))
    The number of rows in the .csv files will be the number of samples in your dataset. Since you're not using batches, the model will evaluate the whole dataset at once every epoch

  2. ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 455, 30), found shape=(None, 30)

    The Tensorflow model expects the first dimension of the input to be the batch size, in the model declaration however they set the input shape to be the same shape as the input. To fix this you can change the input shape of the model to be the number of feature in the dataset.
    model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))
    The number of rows in the .csv files will be the number of samples in your dataset. Since you're not using batches, the model will evaluate the whole dataset at once every epoch

Solution 1

The Tensorflow model expects the first dimension of the input to be the batch size, in the model declaration however they set the input shape to be the same shape as the input. To fix this you can change the input shape of the model to be the number of feature in the dataset.

model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))

The number of rows in the .csv files will be the number of samples in your dataset. Since you’re not using batches, the model will evaluate the whole dataset at once every epoch

Original Author Tobiaaa Of This Content

Solution 2

Args
input_shape Shape tuple (not including the batch axis), or TensorShape instance (not including the batch axis).
the input shape does include batch axis as per documentation of keras
so try giving input_shape=(30,) instead of input_shape=(455,30)

Original Author sangwan Of This Content

Conclusion

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