title: AttributeError: module 'tensorflow' has no attribute 'lite' in Keras model to Tensorflow Lite convertion - Python
body: <p>I have tried to convert following keras model into <strong>tflite</strong> for hosting in a mobile platform using the following code. I have installed tensorflow version=1.12 python version=3.6.7 keras version=2.2.4
  When I have run this code, I have got following error.</p>
  
  <p><strong>converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
  AttributeError: module 'tensorflow' has no attribute 'lite'</strong></p>
  
  <p>What could be the reason for this error and how to solve it?</p>
  
  <pre><code>from keras.preprocessing.image import ImageDataGenerator
  from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D
  from keras.layers import Activation, Dropout, Flatten, Dense
  from keras import backend as K
  import tensorflow as tf
  
  # dimensions of our images.
  img_width, img_height = 150, 150
  
  train_data_dir = 'D:\\My Projects\\Dataset\\dataset6_2clz\\train'
  validation_data_dir = 'D:\\My Projects\\Dataset\\dataset6_2clz\\validation'
  
  
  
  nb_train_samples = 75
  nb_validation_samples = 50
  #epochs = 50
  #batch_size = 16
  epochs = 5
  batch_size = 4
  
  if K.image_data_format() == 'channels_first':
      input_shape = (3, img_width, img_height)
  else:
      input_shape = (img_width, img_height, 3)
  
  model = Sequential()
  model.add(Conv2D(32, (3, 3), input_shape=input_shape))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Conv2D(32, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Conv2D(64, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Flatten())
  model.add(Dense(64))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(1))
  model.add(Activation('sigmoid'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  
  
  train_datagen = ImageDataGenerator(
      rescale=1. / 255,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True)
  
  
  test_datagen = ImageDataGenerator(rescale=1. / 255)
  
  train_generator = train_datagen.flow_from_directory(
      train_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='binary')
  
  validation_generator = test_datagen.flow_from_directory(
      validation_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='binary')
  
  model.fit_generator(
      train_generator,
      steps_per_epoch=nb_train_samples // batch_size,
      epochs=epochs,
      validation_data=validation_generator,
      validation_steps=nb_validation_samples // batch_size)
  
  
  # Save tf.keras model in HDF5 format.
  keras_file = "7_try.h5"
  model.save('7_try.h5')
  
  
  # Convert to TensorFlow Lite model.
  
  converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
  tflite_model = converter.convert()
  open("converted_model.tflite", "wb").write(tflite_model)
  </code></pre>