Incompatible shapes: [100,1] vs. [100,1,4,1]

Issue

This Content is from Stack Overflow. Question asked by RayzioJax

Someone pls help me, I’ve been stuck with this problem in 2 days, I got not solution anywhere, I have to done this work quick because due is 2 days next

Problem says Incompatible shapes: [100,1] vs. [100,1,4,1] when I try to train my data
I don’t know what cause it, I did try to change anything on the layer but not solved

My model was like this

train_set = windowed_dataset(x_train, window_size=60, batch_size=100, shuffle_buffer=1000)
model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(100, 4,)),
    tf.keras.layers.Normalization(axis=None),
    # tf.keras.layers.LSTM(64, return_sequences=True),
    # tf.keras.layers.LSTM(64),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(60, return_sequences=True)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(60)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(120, activation="relu"),
    tf.keras.layers.Dense(100, activation="relu"),
    tf.keras.layers.Dense(60, activation="relu"),
    tf.keras.layers.Dropout(.5),
    tf.keras.layers.Dense(30, activation="relu"),
    tf.keras.layers.Dense(10, activation="relu"),
    tf.keras.layers.Dense(1, input_shape=[None, 1, 1]),
])
model.summary()

And here is the compiling code

optimizer = tf.keras.optimizers.SGD(learning_rate=.01, momentum=.9, decay=.01)
model.compile(
            loss=tf.keras.losses.Huber(),
            optimizer=optimizer,
            metrics=["mae"])

print('Samples : %d' % len(x_train))
history = model.fit(train_set, epochs=50, steps_per_epoch=5, callbacks=[under_mae()])

I’d appreciate the help and explanation from anyone, thank you (I’m sorry if my english bad).



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