# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=invalid-name """VGG19 model for Keras. Reference: - [Very Deep Convolutional Networks for Large-Scale Image Recognition]( https://arxiv.org/abs/1409.1556) (ICLR 2015) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.keras import backend from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.engine import training from tensorflow.python.keras.layers import VersionAwareLayers from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.lib.io import file_io from tensorflow.python.util.tf_export import keras_export WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/keras-applications/' 'vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5') WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/' 'keras-applications/vgg19/' 'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5') layers = VersionAwareLayers() @keras_export('keras.applications.vgg19.VGG19', 'keras.applications.VGG19') def VGG19( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax'): """Instantiates the VGG19 architecture. Reference: - [Very Deep Convolutional Networks for Large-Scale Image Recognition]( https://arxiv.org/abs/1409.1556) (ICLR 2015) By default, it loads weights pre-trained on ImageNet. Check 'weights' for other options. This model can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels). The default input size for this model is 224x224. Note: each Keras Application expects a specific kind of input preprocessing. For VGG19, call `tf.keras.applications.vgg19.preprocess_input` on your inputs before passing them to the model. Arguments: include_top: whether to include the 3 fully-connected layers 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 `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 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 block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Returns: A `keras.Model` instance. Raises: ValueError: in case of invalid argument for `weights`, or invalid input shape. ValueError: if `classifier_activation` is not `softmax` or `None` when using a pretrained top layer. """ if not (weights in {'imagenet', None} or file_io.file_exists_v2(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 = imagenet_utils.obtain_input_shape( input_shape, default_size=224, min_size=32, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # Block 1 x = layers.Conv2D( 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( img_input) x = layers.Conv2D( 64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = layers.Conv2D( 128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) x = layers.Conv2D( 128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = layers.Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) x = layers.Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) x = layers.Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) x = layers.Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x) x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x) x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) x = layers.Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x) x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: # Classification block x = layers.Flatten(name='flatten')(x) x = layers.Dense(4096, activation='relu', name='fc1')(x) x = layers.Dense(4096, activation='relu', name='fc2')(x) imagenet_utils.validate_activation(classifier_activation, weights) x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = training.Model(inputs, x, name='vgg19') # Load weights. if weights == 'imagenet': if include_top: weights_path = data_utils.get_file( 'vgg19_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', file_hash='cbe5617147190e668d6c5d5026f83318') else: weights_path = data_utils.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) elif weights is not None: model.load_weights(weights) return model @keras_export('keras.applications.vgg19.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input( x, data_format=data_format, mode='caffe') @keras_export('keras.applications.vgg19.decode_predictions') def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__