ValueError：“顺序”层的输入 0 与该层不兼容：预期形状 =（无，33714，12），找到形状 =（无，12）

Question

I am trying to run a simple RNN with some data extracted from a csv file. I have already preprocessed my data and split them into train set and validation set, but I get the error above. This is my network structure and what I tryied so far. My shapes are (33714,12) for x_train, (33714,) for y_train, (3745,12) for x_val and (3745,) for y_val.

model = Sequential()
# LSTM LAYER IS ADDED TO MODEL WITH 128 CELLS IN IT
model.add(LSTM(128, input_shape=x_train.shape, activation='tanh', return_sequences=True))
model.add(Dropout(0.2))  # 20% DROPOUT ADDED FOR REGULARIZATION
model.add(BatchNormalization())

model.add(LSTM(128, input_shape=x_train.shape, activation='tanh', return_sequences=True))   # ADD ANOTHER LAYER
model.add(Dropout(0.1))
model.add(BatchNormalization())

model.add(LSTM(128, input_shape=x_train.shape, activation='tanh', return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(32, activation='relu'))  # ADD A DENSE LAYER
model.add(Dropout(0.2))

model.add(Dense(2, activation='softmax'))  # FINAL CLASSIFICATION LAYER WITH 2 CLASSES AND SOFTMAX
# ---------------------------------------------------------------------------------------------------

# OPTIMIZER SETTINGS
opt = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE, decay=DECAY)

# MODEL COMPILE
model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# CALLBACKS
tensorboard = TensorBoard(log_dir=f"logs/{NAME}")
filepath = "RNN_Final-{epoch:02d}-{val_acc:.3f}"
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1,
                                                      save_best_only=True, mode='max'))  # save only the best ones

# RUN THE MODEL
history = model.fit(x_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE,
                    validation_data=(x_val, y_val), callbacks=[tensorboard, checkpoint])

Answer 1

Though it will give you a large value, what may be best to do would be to flatten the one with the larger dimension.

A tensorflow.keras.layers.Flatten() will basically make your output shape the values multiplied, ie input: (None, 5, 5) -> Flatten() -> (None, 25)

For your example, this will give you:

(None, 33714,12) -> (None, 404568).

I'm not entirely sure if this will work when you change the shape sizes, but that is how I overcame my issue with incompatible shapes: expected: (None, x), got: (None, y, x).