CNN-LSTM Data Preprocessing Issue

Question

I am trying to take my CNN model and add a LSTM layer. It would be beneficial to do so given my images are ordered in time series. I've loaded each of my images using ImageDataGenerator and flow_from_directory. I am unable to add a TimeDistributed layer to make my model work. Any help would be greatly appreciated!

 model = Sequential()
  model.add(TimeDistributed(Conv2D(16, (3,3), padding='same', strides=(2,2), 
  activation='relu', input_shape = (224,224,3))))
  model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
  model.add(Dropout(0.5))
  model.add(TimeDistributed(Conv2D(32, (3,3), padding='same', strides=(2,2), 
  activation='relu')))
  model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
  model.add(Dropout(0.5))
  model.add(TimeDistributed(Conv2D(64, (3,3), padding='same', strides=(2,2), 
  activation='relu')))
  model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
  model.add(Dropout(0.5))
  model.add(TimeDistributed(Flatten()))
  model.add(LSTM(units=128, return_sequences=False))
  model.add(LSTM(units=64, return_sequences=False))
  model.add(Dense(32))
  model.add(Dense(2, activation='softmax'))
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

  filenames = os.listdir("train/")

  categories = []
  for f_name in filenames:
    decision = f_name.split('.')[0]
    if decision == 'cat':
        categories.append(0)
    if decision == 'dog':
        categories.append(1)

  dataset = pd.DataFrame({
    'filename':filenames,
    'category':categories
  })

  dataset["category"] = dataset["category"].replace({0: 'cat', 1: 'dog'})
  train_df,validate_df = train_test_split(dataset, test_size=0.20,random_state=42)

  train_df = train_df.reset_index(drop=True)
  validate_df = validate_df.reset_index(drop=True)

  total_train = train_df.shape[0]
  total_validate = validate_df.shape[0]

  train_datagen = ImageDataGenerator(
  rescale=1./255,
  horizontal_flip=False)



train_generator = train_datagen.flow_from_directory(train_df,
  "train/",
  x_col='filename',
  y_col='category',
  target_size=img_size,
  color_mode='rgb',
  class_mode='categorical',
  shuffle=True,
  batch_size=batch_size)



validation_datagen = ImageDataGenerator(
  rescale=1./255,
  horizontal_flip=False)



validation_generator = validation_datagen.flow_from_directory(
  validate_df,
  "train/",
  x_col='filename',
  y_col='category',
  target_size=img_size,
  color_mode='rgb',
  class_mode='categorical',
  shuffle=False,
  batch_size=batch_size
  )



model.fit_generator(
    train_generator,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=total_validate//batch_size,
    steps_per_epoch=total_train//batch_size,
    callbacks=callbacks,
    class_weight=class_weight
  )

CNN-LSTM Data Preprocessing Issue

0 Answers