Python data_utils.get_file函数代码示例

def load_data(path='boston_housing.npz', seed=113, test_split=0.2):
  """Loads the Boston Housing dataset.

  Arguments:
      path: path where to cache the dataset locally
          (relative to ~/.keras/datasets).
      seed: Random seed for shuffling the data
          before computing the test split.
      test_split: fraction of the data to reserve as test set.

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  """
  assert 0 <= test_split < 1
  fh = 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5'
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
      file_hash=fh)
  f = np.load(path)
  x = f['x']
  y = f['y']
  f.close()

  np.random.seed(seed)
  indices = np.arange(len(x))
  np.random.shuffle(indices)
  x = x[indices]
  y = y[indices]

  x_train = np.array(x[:int(len(x) * (1 - test_split))])
  y_train = np.array(y[:int(len(x) * (1 - test_split))])
  x_test = np.array(x[int(len(x) * (1 - test_split)):])
  y_test = np.array(y[int(len(x) * (1 - test_split)):])
  return (x_train, y_train), (x_test, y_test)

def decode_predictions(preds, top=5):
  """Decodes the prediction of an ImageNet model.

  Arguments:
      preds: Numpy tensor encoding a batch of predictions.
      top: integer, how many top-guesses to return.

  Returns:
      A list of lists of top class prediction tuples
      `(class_name, class_description, score)`.
      One list of tuples per sample in batch input.

  Raises:
      ValueError: in case of invalid shape of the `pred` array
          (must be 2D).
  """
  global CLASS_INDEX
  if len(preds.shape) != 2 or preds.shape[1] != 1000:
    raise ValueError('`decode_predictions` expects '
                     'a batch of predictions '
                     '(i.e. a 2D array of shape (samples, 1000)). '
                     'Found array with shape: ' + str(preds.shape))
  if CLASS_INDEX is None:
    fpath = get_file('imagenet_class_index.json',
                     CLASS_INDEX_PATH,
                     cache_subdir='models',
                     file_hash='c2c37ea517e94d9795004a39431a14cb')
    CLASS_INDEX = json.load(open(fpath))
  results = []
  for pred in preds:
    top_indices = pred.argsort()[-top:][::-1]
    result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices]
    result.sort(key=lambda x: x[2], reverse=True)
    results.append(result)
  return results

def load_data():
  """Loads the Fashion-MNIST dataset.

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  """
  dirname = os.path.join('datasets', 'fashion-mnist')
  base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
  files = [
      'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
      't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
  ]

  paths = []
  for given_file in files:
    paths.append(
        get_file(given_file, origin=base + given_file, cache_subdir=dirname))

  with gzip.open(paths[0], 'rb') as lbpath:
    y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[1], 'rb') as imgpath:
    x_train = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)

  with gzip.open(paths[2], 'rb') as lbpath:
    y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[3], 'rb') as imgpath:
    x_test = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)

  return (x_train, y_train), (x_test, y_test)

def load_data():
  """Loads CIFAR10 dataset.

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  """
  dirname = 'cifar-10-batches-py'
  origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
  path = get_file(dirname, origin=origin, untar=True)

  num_train_samples = 50000

  x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
  y_train = np.zeros((num_train_samples,), dtype='uint8')

  for i in range(1, 6):
    fpath = os.path.join(path, 'data_batch_' + str(i))
    data, labels = load_batch(fpath)
    x_train[(i - 1) * 10000:i * 10000, :, :, :] = data
    y_train[(i - 1) * 10000:i * 10000] = labels

  fpath = os.path.join(path, 'test_batch')
  x_test, y_test = load_batch(fpath)

  y_train = np.reshape(y_train, (len(y_train), 1))
  y_test = np.reshape(y_test, (len(y_test), 1))

  if K.image_data_format() == 'channels_last':
    x_train = x_train.transpose(0, 2, 3, 1)
    x_test = x_test.transpose(0, 2, 3, 1)

  return (x_train, y_train), (x_test, y_test)

def load_data(label_mode='fine'):
  """Loads CIFAR100 dataset.

  Arguments:
      label_mode: one of "fine", "coarse".

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

  Raises:
      ValueError: in case of invalid `label_mode`.
  """
  if label_mode not in ['fine', 'coarse']:
    raise ValueError('label_mode must be one of "fine" "coarse".')

  dirname = 'cifar-100-python'
  origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
  path = get_file(dirname, origin=origin, untar=True)

  fpath = os.path.join(path, 'train')
  x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')

  fpath = os.path.join(path, 'test')
  x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')

  y_train = np.reshape(y_train, (len(y_train), 1))
  y_test = np.reshape(y_test, (len(y_test), 1))

  if K.image_data_format() == 'channels_last':
    x_train = x_train.transpose(0, 2, 3, 1)
    x_test = x_test.transpose(0, 2, 3, 1)

  return (x_train, y_train), (x_test, y_test)

def get_word_index(path='imdb_word_index.json'):
  """Retrieves the dictionary mapping word indices back to words.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).

  Returns:
      The word index dictionary.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json',
      file_hash='bfafd718b763782e994055a2d397834f')
  with open(path) as f:
    return json.load(f)

def get_word_index(path='imdb_word_index.json'):
  """Retrieves the dictionary mapping word indices back to words.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).

  Returns:
      The word index dictionary.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json')
  f = open(path)
  data = json.load(f)
  f.close()
  return data

def get_word_index(path='reuters_word_index.json'):
  """Retrieves the dictionary mapping word indices back to words.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).

  Returns:
      The word index dictionary.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json',
      file_hash='4d44cc38712099c9e383dc6e5f11a921')
  f = open(path)
  data = json.load(f)
  f.close()
  return data

def load_data(path='mnist.npz'):
  """Loads the MNIST dataset.

  Arguments:
      path: path where to cache the dataset locally
          (relative to ~/.keras/datasets).

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/img-datasets/mnist.npz',
      file_hash='8a61469f7ea1b51cbae51d4f78837e45')
  f = np.load(path)
  x_train, y_train = f['x_train'], f['y_train']
  x_test, y_test = f['x_test'], f['y_test']
  f.close()
  return (x_train, y_train), (x_test, y_test)

def load_data(path='reuters.npz',
              num_words=None,
              skip_top=0,
              maxlen=None,
              test_split=0.2,
              seed=113,
              start_char=1,
              oov_char=2,
              index_from=3):
  """Loads the Reuters newswire classification dataset.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).
      num_words: max number of words to include. Words are ranked
          by how often they occur (in the training set) and only
          the most frequent words are kept
      skip_top: skip the top N most frequently occurring words
          (which may not be informative).
      maxlen: truncate sequences after this length.
      test_split: Fraction of the dataset to be used as test data.
      seed: random seed for sample shuffling.
      start_char: The start of a sequence will be marked with this character.
          Set to 1 because 0 is usually the padding character.
      oov_char: words that were cut out because of the `num_words`
          or `skip_top` limit will be replaced with this character.
      index_from: index actual words with this index and higher.

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

  Note that the 'out of vocabulary' character is only used for
  words that were present in the training set but are not included
  because they're not making the `num_words` cut here.
  Words that were not seen in the training set but are in the test set
  have simply been skipped.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/text-datasets/reuters.npz',
      file_hash='87aedbeb0cb229e378797a632c1997b6')
  npzfile = np.load(path)
  xs = npzfile['x']
  labels = npzfile['y']
  npzfile.close()

  np.random.seed(seed)
  indices = np.arrange(len(xs))
  np.random.shuffle(indices)
  xs = xs[indices]
  labels = labels[indices]

  np.random.shuffle(labels)

  if start_char is not None:
    xs = [[start_char] + [w + index_from for w in x] for x in xs]
  elif index_from:
    xs = [[w + index_from for w in x] for x in xs]

  if maxlen:
    new_xs = []
    new_labels = []
    for x, y in zip(xs, labels):
      if len(x) < maxlen:
        new_xs.append(x)
        new_labels.append(y)
    xs = new_xs
    labels = new_labels

  if not num_words:
    num_words = max([max(x) for x in xs])

  # by convention, use 2 as OOV word
  # reserve 'index_from' (=3 by default) characters:
  # 0 (padding), 1 (start), 2 (OOV)
  if oov_char is not None:
    xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x]
          for x in xs]
  else:
    new_xs = []
    for x in xs:
      nx = []
      for w in x:
        if skip_top <= w < num_words:
          nx.append(w)
      new_xs.append(nx)
    xs = new_xs

  x_train = np.array(xs[:int(len(xs) * (1 - test_split))])
  y_train = np.array(labels[:int(len(xs) * (1 - test_split))])

  x_test = np.array(xs[int(len(xs) * (1 - test_split)):])
  y_test = np.array(labels[int(len(xs) * (1 - test_split)):])

  return (x_train, y_train), (x_test, y_test)

def load_data(path='imdb.npz',
              num_words=None,
              skip_top=0,
              maxlen=None,
              seed=113,
              start_char=1,
              oov_char=2,
              index_from=3):
  """Loads the IMDB dataset.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).
      num_words: max number of words to include. Words are ranked
          by how often they occur (in the training set) and only
          the most frequent words are kept
      skip_top: skip the top N most frequently occurring words
          (which may not be informative).
      maxlen: sequences longer than this will be filtered out.
      seed: random seed for sample shuffling.
      start_char: The start of a sequence will be marked with this character.
          Set to 1 because 0 is usually the padding character.
      oov_char: words that were cut out because of the `num_words`
          or `skip_top` limit will be replaced with this character.
      index_from: index actual words with this index and higher.

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

  Raises:
      ValueError: in case `maxlen` is so low
          that no input sequence could be kept.

  Note that the 'out of vocabulary' character is only used for
  words that were present in the training set but are not included
  because they're not making the `num_words` cut here.
  Words that were not seen in the training set but are in the test set
  have simply been skipped.
  """
  path = get_file(
      path,
      origin='https://s3.amazonaws.com/text-datasets/imdb.npz',
      file_hash='599dadb1135973df5b59232a0e9a887c')
  f = np.load(path)
  x_train, labels_train = f['x_train'], f['y_train']
  x_test, labels_test = f['x_test'], f['y_test']
  f.close()

  np.random.seed(seed)
  indices = np.arrange(len(x_train))
  np.random.shuffle(indices)
  x_train = x_train[indices]
  labels_train = labels_train[indices]

  indices = np.arrange(len(x_test))
  np.random.shuffle(indices)
  x_test = x_test[indices]
  labels_test = labels_test[indices]

  xs = np.concatenate([x_train, x_test])
  labels = np.concatenate([labels_train, labels_test])

  if start_char is not None:
    xs = [[start_char] + [w + index_from for w in x] for x in xs]
  elif index_from:
    xs = [[w + index_from for w in x] for x in xs]

  if maxlen:
    new_xs = []
    new_labels = []
    for x, y in zip(xs, labels):
      if len(x) < maxlen:
        new_xs.append(x)
        new_labels.append(y)
    xs = new_xs
    labels = new_labels
    if not xs:
      raise ValueError('After filtering for sequences shorter than maxlen=' +
                       str(maxlen) + ', no sequence was kept. '
                       'Increase maxlen.')
  if not num_words:
    num_words = max([max(x) for x in xs])

  # by convention, use 2 as OOV word
  # reserve 'index_from' (=3 by default) characters:
  # 0 (padding), 1 (start), 2 (OOV)
  if oov_char is not None:
    xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x]
          for x in xs]
  else:
    new_xs = []
    for x in xs:
      nx = []
      for w in x:
        if skip_top <= w < num_words:
          nx.append(w)
      new_xs.append(nx)
    xs = new_xs

  x_train = np.array(xs[:len(x_train)])
  y_train = np.array(labels[:len(x_train)])
#.........这里部分代码省略.........

#.........这里部分代码省略.........
  x = SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
  x = BatchNormalization(name='block4_sepconv1_bn')(x)
  x = Activation('relu', name='block4_sepconv2_act')(x)
  x = SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
  x = BatchNormalization(name='block4_sepconv2_bn')(x)

  x = MaxPooling2D(
      (3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
  x = layers.add([x, residual])

  for i in range(8):
    residual = x
    prefix = 'block' + str(i + 5)

    x = Activation('relu', name=prefix + '_sepconv1_act')(x)
    x = SeparableConv2D(
        728, (3, 3), padding='same', use_bias=False,
        name=prefix + '_sepconv1')(x)
    x = BatchNormalization(name=prefix + '_sepconv1_bn')(x)
    x = Activation('relu', name=prefix + '_sepconv2_act')(x)
    x = SeparableConv2D(
        728, (3, 3), padding='same', use_bias=False,
        name=prefix + '_sepconv2')(x)
    x = BatchNormalization(name=prefix + '_sepconv2_bn')(x)
    x = Activation('relu', name=prefix + '_sepconv3_act')(x)
    x = SeparableConv2D(
        728, (3, 3), padding='same', use_bias=False,
        name=prefix + '_sepconv3')(x)
    x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)

    x = layers.add([x, residual])

  residual = Conv2D(
      1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
  residual = BatchNormalization()(residual)

  x = Activation('relu', name='block13_sepconv1_act')(x)
  x = SeparableConv2D(
      728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
  x = BatchNormalization(name='block13_sepconv1_bn')(x)
  x = Activation('relu', name='block13_sepconv2_act')(x)
  x = SeparableConv2D(
      1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
  x = BatchNormalization(name='block13_sepconv2_bn')(x)

  x = MaxPooling2D(
      (3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
  x = layers.add([x, residual])

  x = SeparableConv2D(
      1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
  x = BatchNormalization(name='block14_sepconv1_bn')(x)
  x = Activation('relu', name='block14_sepconv1_act')(x)

  x = SeparableConv2D(
      2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
  x = BatchNormalization(name='block14_sepconv2_bn')(x)
  x = Activation('relu', name='block14_sepconv2_act')(x)

  if include_top:
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = GlobalMaxPooling2D()(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = get_source_inputs(input_tensor)
  else:
    inputs = img_input
  # Create model.
  model = Model(inputs, x, name='xception')

  # load weights
  if weights == 'imagenet':
    if include_top:
      weights_path = get_file(
          'xception_weights_tf_dim_ordering_tf_kernels.h5',
          TF_WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
    else:
      weights_path = get_file(
          'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
          TF_WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='b0042744bf5b25fce3cb969f33bebb97')
    model.load_weights(weights_path)

  if old_data_format:
    K.set_image_data_format(old_data_format)
  elif weights is not None:
    model.load_weights(weights)
  return model

#.........这里部分代码省略.........
      weights=weights)

  if input_tensor is None:
    img_input = Input(shape=input_shape)
  else:
    if not K.is_keras_tensor(input_tensor):
      img_input = Input(tensor=input_tensor, shape=input_shape)
    else:
      img_input = input_tensor

  bn_axis = 3 if K.image_data_format() == 'channels_last' else 1

  x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
  x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
  x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
  x = Activation('relu', name='conv1/relu')(x)
  x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
  x = MaxPooling2D(3, strides=2, name='pool1')(x)

  x = dense_block(x, blocks[0], name='conv2')
  x = transition_block(x, 0.5, name='pool2')
  x = dense_block(x, blocks[1], name='conv3')
  x = transition_block(x, 0.5, name='pool3')
  x = dense_block(x, blocks[2], name='conv4')
  x = transition_block(x, 0.5, name='pool4')
  x = dense_block(x, blocks[3], name='conv5')

  x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)

  if include_top:
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='fc1000')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D(name='avg_pool')(x)
    elif pooling == 'max':
      x = GlobalMaxPooling2D(name='max_pool')(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = get_source_inputs(input_tensor)
  else:
    inputs = img_input

  # Create model.
  if blocks == [6, 12, 24, 16]:
    model = Model(inputs, x, name='densenet121')
  elif blocks == [6, 12, 32, 32]:
    model = Model(inputs, x, name='densenet169')
  elif blocks == [6, 12, 48, 32]:
    model = Model(inputs, x, name='densenet201')
  else:
    model = Model(inputs, x, name='densenet')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      if blocks == [6, 12, 24, 16]:
        weights_path = get_file(
            'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET121_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='0962ca643bae20f9b6771cb844dca3b0')
      elif blocks == [6, 12, 32, 32]:
        weights_path = get_file(
            'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET169_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='bcf9965cf5064a5f9eb6d7dc69386f43')
      elif blocks == [6, 12, 48, 32]:
        weights_path = get_file(
            'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET201_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='7bb75edd58cb43163be7e0005fbe95ef')
    else:
      if blocks == [6, 12, 24, 16]:
        weights_path = get_file(
            'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET121_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3')
      elif blocks == [6, 12, 32, 32]:
        weights_path = get_file(
            'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET169_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='50662582284e4cf834ce40ab4dfa58c6')
      elif blocks == [6, 12, 48, 32]:
        weights_path = get_file(
            'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET201_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='1c2de60ee40562448dbac34a0737e798')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model

#.........这里部分代码省略.........
      128, (3, 3), activation='relu', padding='same', name='block2_conv2')(
          x)
  x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

  # Block 3
  x = Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv1')(
          x)
  x = Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv2')(
          x)
  x = Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv3')(
          x)
  x = Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv4')(
          x)
  x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

  # Block 4
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv1')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv2')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv3')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv4')(
          x)
  x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

  # Block 5
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv1')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv2')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv3')(
          x)
  x = Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv4')(
          x)
  x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

  if include_top:
    # Classification block
    x = Flatten(name='flatten')(x)
    x = Dense(4096, activation='relu', name='fc1')(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = GlobalMaxPooling2D()(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = get_source_inputs(input_tensor)
  else:
    inputs = img_input
  # Create model.
  model = Model(inputs, x, name='vgg19')

  # load weights
  if weights == 'imagenet':
    if include_top:
      weights_path = get_file(
          'vgg19_weights_tf_dim_ordering_tf_kernels.h5',
          WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='cbe5617147190e668d6c5d5026f83318')
    else:
      weights_path = get_file(
          'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
          WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='253f8cb515780f3b799900260a226db6')
    model.load_weights(weights_path)
    if K.backend() == 'theano':
      layer_utils.convert_all_kernels_in_model(model)

    if K.image_data_format() == 'channels_first':
      if include_top:
        maxpool = model.get_layer(name='block5_pool')
        shape = maxpool.output_shape[1:]
        dense = model.get_layer(name='fc1')
        layer_utils.convert_dense_weights_data_format(dense, shape,
                                                      'channels_first')

  elif weights is not None:
    model.load_weights(weights)

  return model

#.........这里部分代码省略.........
    x = Conv2D(
        stem_block_filters, (3, 3),
        strides=(1, 1),
        padding='same',
        use_bias=False,
        name='stem_conv1',
        kernel_initializer='he_normal')(
            img_input)

  x = BatchNormalization(
      axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')(
          x)

  p = None
  if not skip_reduction:  # imagenet / mobile mode
    x, p = _reduction_a_cell(
        x, p, filters // (filter_multiplier**2), block_id='stem_1')
    x, p = _reduction_a_cell(
        x, p, filters // filter_multiplier, block_id='stem_2')

  for i in range(num_blocks):
    x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i))

  x, p0 = _reduction_a_cell(
      x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks))

  p = p0 if not skip_reduction else p

  for i in range(num_blocks):
    x, p = _normal_a_cell(
        x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1))

  x, p0 = _reduction_a_cell(
      x,
      p,
      filters * filter_multiplier**2,
      block_id='reduce_%d' % (2 * num_blocks))

  p = p0 if not skip_reduction else p

  for i in range(num_blocks):
    x, p = _normal_a_cell(
        x,
        p,
        filters * filter_multiplier**2,
        block_id='%d' % (2 * num_blocks + i + 1))

  x = Activation('relu')(x)

  if include_top:
    x = GlobalAveragePooling2D()(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = GlobalMaxPooling2D()(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = get_source_inputs(input_tensor)
  else:
    inputs = img_input

  model = Model(inputs, x, name='NASNet')

  # load weights
  if weights == 'imagenet':
    if default_size == 224:  # mobile version
      if include_top:
        weight_path = NASNET_MOBILE_WEIGHT_PATH
        model_name = 'nasnet_mobile.h5'
      else:
        weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP
        model_name = 'nasnet_mobile_no_top.h5'

      weights_file = get_file(model_name, weight_path, cache_subdir='models')
      model.load_weights(weights_file)

    elif default_size == 331:  # large version
      if include_top:
        weight_path = NASNET_LARGE_WEIGHT_PATH
        model_name = 'nasnet_large.h5'
      else:
        weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP
        model_name = 'nasnet_large_no_top.h5'

      weights_file = get_file(model_name, weight_path, cache_subdir='models')
      model.load_weights(weights_file)
    else:
      raise ValueError('ImageNet weights can only be loaded with NASNetLarge'
                       ' or NASNetMobile')
  elif weights is not None:
    model.load_weights(weights)

  if old_data_format:
    K.set_image_data_format(old_data_format)

  return model