Prompt Title: ABC

This error message indicates that there is a mismatch in the input dimensions of the LSTM layer in your model.

LSTM layers in Keras/TensorFlow expect input data to have 3 dimensions:

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(batch_size, timesteps, input_dim)

However, it seems that the input data you are passing has only 2 dimensions:

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(32, 128)

Here, (32, 128) means that you have a batch of 32 sequences, each with 128 features.

To fix this error, you need to reshape your input data to have 3 dimensions. Depending on the format of your input data, you may need to use a function like np.reshape() or tf.reshape() to do this.

Assuming that your input data has shape (batch_size, input_dim), you can reshape it to have the required format using:

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num_timesteps = 1  # Assuming you have only one timestep
input_dim = 128    # Assuming each sequence has 128 features

reshaped_input = np.reshape(input_data, (batch_size, num_timesteps, input_dim))

This will give you input data of shape (batch_size, 1, 128), which you can pass to the LSTM layer.

This error message indicates that there is a shape mismatch between the input data and the expected input shape of the LSTM layer lstm_23.

In this code, lstm_23 refers to the third LSTM layer in your model. The error message is telling you that this layer expects a 3D input with shape (batch_size, timesteps, input_dim), but it received a 2D input with shape (32, 128).

The input shape of your model is (batch_size, 10080, 74), which means that each input sample has 10080 timesteps and 74 input features. However, in the first LSTM layer, you specified the input_shape as (10080, 74) which is redundant as you have already defined it in the Input layer.

To fix this error, you need to remove the input_shape argument from the first LSTM layer and also make sure that your input data has the correct shape. The shape of your input data should be (batch_size, 10080, 74).

Here's the modified code:

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inputs = tf.keras.layers.Input(shape=(10080, 74), batch_size=BATCH_SIZE)

lstm_l1, state_h, state_c = LSTM(256, activation='relu', return_sequences=True, return_state=True)(inputs)
lstm_l2= LSTM(128, activation='relu')(lstm_l1)
lstm_l3 = LSTM(64, activation='relu')(lstm_l2)
dense_1 = Dense(1000, activation="relu")(lstm_l3)
outputs = Dense(1, activation="sigmoid")(dense_1)

model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()

Make sure that your input data has the shape (batch_size, 10080, 74) before passing it to the model.

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