Python models.Model类代码示例

#.........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.........
      ValueError: in case of invalid argument for `weights`,
          or invalid input shape.
  """
  if not (weights in {'imagenet', None} or os.path.exists(weights)):
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization), `imagenet` '
                     '(pre-training on ImageNet), '
                     'or the path to the weights file to be loaded.')

  if weights == 'imagenet' and include_top and classes != 1000:
    raise ValueError('If using `weights` as imagenet with `include_top`'
                     ' as true, `classes` should be 1000')

  # Determine proper input shape
  input_shape = _obtain_input_shape(
      input_shape,
      default_size=224,
      min_size=197,
      data_format=K.image_data_format(),
      require_flatten=include_top,
      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
  if K.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1

  x = Conv2D(
      64, (7, 7), strides=(2, 2), padding='same', name='conv1')(
          img_input)
  x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)

  x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

  x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

  x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

  x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

  x = AveragePooling2D((7, 7), name='avg_pool')(x)

  if include_top:
    x = Flatten()(x)
    x = Dense(classes, activation='softmax', name='fc1000')(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='resnet50')

  # load weights
  if weights == 'imagenet':
    if include_top:
      weights_path = get_file(
          'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
          WEIGHTS_PATH,
          cache_subdir='models',
          md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
    else:
      weights_path = get_file(
          'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
          WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          md5_hash='a268eb855778b3df3c7506639542a6af')
    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

def InceptionResNetV2(include_top=True,
                      weights='imagenet',
                      input_tensor=None,
                      input_shape=None,
                      pooling=None,
                      classes=1000):
  """Instantiates the Inception-ResNet v2 architecture.

  Optionally loads weights pre-trained on ImageNet.
  Note that when using TensorFlow, for best performance you should
  set `"image_data_format": "channels_last"` in your Keras config
  at `~/.keras/keras.json`.

  The model and the weights are compatible with TensorFlow, Theano and
  CNTK backends. The data format convention used by the model is
  the one specified in your Keras config file.

  Note that the default input image size for this model is 299x299, instead
  of 224x224 as in the VGG16 and ResNet models. Also, the input preprocessing
  function is different (i.e., do not use `imagenet_utils.preprocess_input()`
  with this model. Use `preprocess_input()` defined in this module instead).

  Arguments:
      include_top: whether to include the fully-connected
          layer at the top of the network.
      weights: one of `None` (random initialization),
            'imagenet' (pre-training on ImageNet),
            or the path to the weights file to be loaded.
      input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
          to use as image input for the model.
      input_shape: optional shape tuple, only to be specified
          if `include_top` is `False` (otherwise the input shape
          has to be `(299, 299, 3)` (with `'channels_last'` data format)
          or `(3, 299, 299)` (with `'channels_first'` data format).
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 139.
          E.g. `(150, 150, 3)` would be one valid value.
      pooling: Optional pooling mode for feature extraction
          when `include_top` is `False`.
          - `None` means that the output of the model will be
              the 4D tensor output of the last convolutional layer.
          - `'avg'` means that global average pooling
              will be applied to the output of the
              last convolutional layer, and thus
              the output of the model will be a 2D tensor.
          - `'max'` means that global max pooling will be applied.
      classes: optional number of classes to classify images
          into, only to be specified if `include_top` is `True`, and
          if no `weights` argument is specified.

  Returns:
      A Keras `Model` instance.

  Raises:
      ValueError: in case of invalid argument for `weights`,
          or invalid input shape.
  """
  if not (weights in {'imagenet', None} or os.path.exists(weights)):
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization), `imagenet` '
                     '(pre-training on ImageNet), '
                     'or the path to the weights file to be loaded.')

  if weights == 'imagenet' and include_top and classes != 1000:
    raise ValueError('If using `weights` as imagenet with `include_top`'
                     ' as true, `classes` should be 1000')

  # Determine proper input shape
  input_shape = _obtain_input_shape(
      input_shape,
      default_size=299,
      min_size=139,
      data_format=K.image_data_format(),
      require_flatten=False,
      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

  # Stem block: 35 x 35 x 192
  x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
  x = conv2d_bn(x, 32, 3, padding='valid')
  x = conv2d_bn(x, 64, 3)
  x = MaxPooling2D(3, strides=2)(x)
  x = conv2d_bn(x, 80, 1, padding='valid')
  x = conv2d_bn(x, 192, 3, padding='valid')
  x = MaxPooling2D(3, strides=2)(x)

  # Mixed 5b (Inception-A block): 35 x 35 x 320
  branch_0 = conv2d_bn(x, 96, 1)
  branch_1 = conv2d_bn(x, 48, 1)
  branch_1 = conv2d_bn(branch_1, 64, 5)
  branch_2 = conv2d_bn(x, 64, 1)
  branch_2 = conv2d_bn(branch_2, 96, 3)
  branch_2 = conv2d_bn(branch_2, 96, 3)
#.........这里部分代码省略.........

#.........这里部分代码省略.........
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=channel_axis,
        name='mixed' + str(5 + i))

  # mixed 7: 17 x 17 x 768
  branch1x1 = conv2d_bn(x, 192, 1, 1)

  branch7x7 = conv2d_bn(x, 192, 1, 1)
  branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
  branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

  branch7x7dbl = conv2d_bn(x, 192, 1, 1)
  branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
  branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
  branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

  branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
  branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
  x = layers.concatenate(
      [branch1x1, branch7x7, branch7x7dbl, branch_pool],
      axis=channel_axis,
      name='mixed7')

  # mixed 8: 8 x 8 x 1280
  branch3x3 = conv2d_bn(x, 192, 1, 1)
  branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid')

  branch7x7x3 = conv2d_bn(x, 192, 1, 1)
  branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
  branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
  branch7x7x3 = conv2d_bn(
      branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')

  branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
  x = layers.concatenate(
      [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8')

  # mixed 9: 8 x 8 x 2048
  for i in range(2):
    branch1x1 = conv2d_bn(x, 320, 1, 1)

    branch3x3 = conv2d_bn(x, 384, 1, 1)
    branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
    branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
    branch3x3 = layers.concatenate(
        [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i))

    branch3x3dbl = conv2d_bn(x, 448, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
    branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
    branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
    branch3x3dbl = layers.concatenate(
        [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis)

    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch3x3, branch3x3dbl, branch_pool],
        axis=channel_axis,
        name='mixed' + str(9 + i))
  if include_top:
    # Classification block
    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='inception_v3')

  # load weights
  if weights == 'imagenet':
    if include_top:
      weights_path = get_file(
          'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
          WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='9a0d58056eeedaa3f26cb7ebd46da564')
    else:
      weights_path = get_file(
          'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
          WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='bcbd6486424b2319ff4ef7d526e38f63')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model

#.........这里部分代码省略.........
                    '(width, height, channels). '
                    'However your settings specify the default '
                    'data format "channels_first" (channels, width, height).'
                    ' You should set `image_data_format="channels_last"` '
                    'in your Keras config located at ~/.keras/keras.json. '
                    'The model being returned right now will expect inputs '
                    'to follow the "channels_last" data format.')
    K.set_image_data_format('channels_last')
    old_data_format = 'channels_first'
  else:
    old_data_format = None

  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

  x = _conv_block(img_input, 32, alpha, strides=(2, 2))
  x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)

  x = _depthwise_conv_block(
      x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2)
  x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)

  x = _depthwise_conv_block(
      x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4)
  x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)

  x = _depthwise_conv_block(
      x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)

  x = _depthwise_conv_block(
      x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12)
  x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)

  if include_top:
    if K.image_data_format() == 'channels_first':
      shape = (int(1024 * alpha), 1, 1)
    else:
      shape = (1, 1, int(1024 * alpha))

    x = GlobalAveragePooling2D()(x)
    x = Reshape(shape, name='reshape_1')(x)
    x = Dropout(dropout, name='dropout')(x)
    x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x)
    x = Activation('softmax', name='act_softmax')(x)
    x = Reshape((classes,), name='reshape_2')(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='mobilenet_%0.2f_%s' % (alpha, rows))

  # load weights
  if weights == 'imagenet':
    if K.image_data_format() == 'channels_first':
      raise ValueError('Weights for "channels_last" format '
                       'are not available.')
    if alpha == 1.0:
      alpha_text = '1_0'
    elif alpha == 0.75:
      alpha_text = '7_5'
    elif alpha == 0.50:
      alpha_text = '5_0'
    else:
      alpha_text = '2_5'

    if include_top:
      model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows)
      weigh_path = BASE_WEIGHT_PATH + model_name
      weights_path = get_file(model_name, weigh_path, cache_subdir='models')
    else:
      model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows)
      weigh_path = BASE_WEIGHT_PATH + model_name
      weights_path = get_file(model_name, weigh_path, cache_subdir='models')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  if old_data_format:
    K.set_image_data_format(old_data_format)
  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

#.........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