Python Model.load_weights方法代码示例

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........这里部分代码省略.........
  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)
  branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
  branch_pool = conv2d_bn(branch_pool, 64, 1)
  branches = [branch_0, branch_1, branch_2, branch_pool]
  channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
  x = Concatenate(axis=channel_axis, name='mixed_5b')(branches)

  # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
  for block_idx in range(1, 11):
    x = inception_resnet_block(
        x, scale=0.17, block_type='block35', block_idx=block_idx)

  # Mixed 6a (Reduction-A block): 17 x 17 x 1088
  branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
  branch_1 = conv2d_bn(x, 256, 1)
  branch_1 = conv2d_bn(branch_1, 256, 3)
  branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
  branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
  branches = [branch_0, branch_1, branch_pool]
  x = Concatenate(axis=channel_axis, name='mixed_6a')(branches)

  # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
  for block_idx in range(1, 21):
    x = inception_resnet_block(
        x, scale=0.1, block_type='block17', block_idx=block_idx)

  # Mixed 7a (Reduction-B block): 8 x 8 x 2080
  branch_0 = conv2d_bn(x, 256, 1)
  branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
  branch_1 = conv2d_bn(x, 256, 1)
  branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
  branch_2 = conv2d_bn(x, 256, 1)
  branch_2 = conv2d_bn(branch_2, 288, 3)
  branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
  branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
  branches = [branch_0, branch_1, branch_2, branch_pool]
  x = Concatenate(axis=channel_axis, name='mixed_7a')(branches)

  # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
  for block_idx in range(1, 10):
    x = inception_resnet_block(
        x, scale=0.2, block_type='block8', block_idx=block_idx)
  x = inception_resnet_block(
      x, scale=1., activation=None, block_type='block8', block_idx=10)

  # Final convolution block: 8 x 8 x 1536
  x = conv2d_bn(x, 1536, 1, name='conv_7b')

  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_resnet_v2')

  # Load weights
  if weights == 'imagenet':
    if include_top:
      fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
      weights_path = get_file(
          fname,
          BASE_WEIGHT_URL + fname,
          cache_subdir='models',
          file_hash='e693bd0210a403b3192acc6073ad2e96')
    else:
      fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5'
      weights_path = get_file(
          fname,
          BASE_WEIGHT_URL + fname,
          cache_subdir='models',
          file_hash='d19885ff4a710c122648d3b5c3b684e4')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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

# 需要导入模块: from tensorflow.python.keras._impl.keras.models import Model [as 别名]
# 或者: from tensorflow.python.keras._impl.keras.models.Model import load_weights [as 别名]

#.........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