title: What should I replace "test_images" and "test_labels" with inside this Convolutional Neural Network Machine Learning Model?
body: <p>What should I replace &quot;test_images&quot; and &quot;test_labels&quot; with inside this Convolutional Neural Network Machine Learning Model?</p>
  <p>I trained my model and saved it using this code:</p>
  <pre class="lang-py prettyprint-override"><code>import numpy as np
  import tensorflow as tf
  from tensorflow import keras
  from tensorflow.keras import layers
  from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D
  from keras.layers import Activation, Dropout, Flatten, Dense
  
  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
  
  
  # dimensions of our images.
  img_width, img_height = 150, 150
  
  train_data_dir = 'data/train'
  validation_data_dir = 'data/validation'
  nb_train_samples = 4000
  nb_validation_samples = 800
  epochs = 50
  batch_size = 18
  
  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'])
  
  # this is the augmentation configuration we will use for training
  train_datagen = ImageDataGenerator(
      rescale=1. / 255,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True)
  
  # this is the augmentation configuration we will use for testing:
  # only rescaling
  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)
  
  model.save_weights('first_try.h5')
  # Saving Model
  model.save(&quot;modeln1.tf&quot;)
  </code></pre>
  <p>After that, I decided to load my model and print out its summary and accuracy using this code:</p>
  <pre class="lang-py prettyprint-override"><code>import tensorflow as tf
  import keras
  import os
  
  new_model = tf.keras.models.load_model('/Users/test/Documents/DigiDoctorTF/modeln1.tf')
  
  new_model.summary()
  
  loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)
  
  print('Accuracy: {:5.2f}%'.format(100*acc))
  </code></pre>
  <p>I tried this with both the model and the weights file. However, I received this error:</p>
  <pre><code>Traceback (most recent call last):
    File &quot;ld1.py&quot;, line 9, in &lt;module&gt;
      loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)
  NameError: name 'test_images' is not defined
  </code></pre>
  <p>What is this error?  What do I replace test_images with?</p>