title: Keras Convolution2D Input: Error when checking model input: expected convolution2d_input_1 to have shape
body: <p>I am working through <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">this great tutorial</a> on creating an image classifier using Keras.  Once I have trained the model, I save it to a file and then later reload it into a model in a test script shown below.</p>
  
  <p>I get the following exception when I evaluate the model using a new, never-before-seen image:</p>
  
  <p>Error:</p>
  
  <pre><code>Traceback (most recent call last):
    File "test_classifier.py", line 48, in &lt;module&gt;
      score = model.evaluate(x, y, batch_size=16)
    File "/Library/Python/2.7/site-packages/keras/models.py", line 655, in evaluate
      sample_weight=sample_weight)
    File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 1131, in evaluate
      batch_size=batch_size)
    File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 959, in _standardize_user_data
  exception_prefix='model input')
    File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 108, in standardize_input_data
  str(array.shape))
  Exception: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 150, 150) but got array with shape (1, 3, 150, 198)`
  </code></pre>
  
  <p>Is the problem with the model that I have trained or with how I am invoking the evaluate method?</p>
  
  <p>Code:</p>
  
  <pre><code>    from keras.preprocessing.image import ImageDataGenerator
      from keras.models import Sequential
      from keras.layers import Convolution2D, MaxPooling2D
      from keras.layers import Activation, Dropout, Flatten, Dense
      from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
  
      import numpy as np
      img_width, img_height = 150, 150
      train_data_dir = 'data/train'
      validation_data_dir = 'data/validation'
      nb_train_samples = 2000
      nb_validation_samples = 800
      nb_epoch = 5
      model = Sequential()
      model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height)))
      model.add(Activation('relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Convolution2D(32, 3, 3))
      model.add(Activation('relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Convolution2D(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'])
      model.load_weights('first_try.h5')
      img = load_img('data/test2/ferrari.jpeg')
      x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
      x = x.reshape( (1,) + x.shape )  # this is a Numpy array with shape (1, 3, 150, 150)
      y = np.array([0])
      score = model.evaluate(x, y, batch_size=16)`
  </code></pre>