Why do you define the last dimension of input_shape as $3$? Just put your desired input dimensions accordingly and it should be fine:

input_shape = (img_width, img_height)

Why do you define the last dimension of input_shape as $3$? Just put your desired input dimensions accordingly and it should be fine:

input_shape = (img_width, img_height)

Update with the full code:

The best way would be to use TimeseriesGenerator instead of ImageDataGenerator but there seems there not flow_from_directory method meeting your needs. So, I think the best solution is to squeeze the last dimension of the generator output. Also, you have a color_mode option that allows to generate a 1-channel only tensor for grayscale images. Full code of concerned parts:

model = Sequential()
model.add(Lambda(lambda x: x[:,:,:,0], input_shape=(*input_shape, 1)))
model.add(LSTM(units=256, return_sequences=True))
model.add(LSTM(units=128, return_sequences=True))
model.add(LSTM(units=64))
model.add(Dense(128))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])


train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode='grayscale')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode='grayscale')

Why do you define the last dimension of input_shape as $3$? Just put your desired input dimensions accordingly $(3601, 217)$ and it should be fine.

Why do you define the last dimension of input_shape as $3$? Just put your desired input dimensions accordingly and it should be fine:

input_shape = (img_width, img_height)