inception_v4 Namespace Reference

Functions
def	preprocess_input (x)
def	conv2d_bn (x, nb_filter, num_row, num_col, padding='same', strides=(1, 1), use_bias=False)
def	block_inception_a (input)
def	block_reduction_a (input)
def	block_inception_b (input)
def	block_reduction_b (input)
def	block_inception_c (input)
def	inception_v4_base (input)
def	inception_v4 (num_classes, dropout_keep_prob, weights, input_shape, include_top, transfer_learning=None)
def	create_model (num_classes=1001, dropout_prob=0.2, weights=None, include_top=True, transfer_learning=None, input_shape=None)

Variables
string	WEIGHTS_PATH = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels.h5'
	Implements the Inception Network v4 (http://arxiv.org/pdf/1602.07261v1.pdf) in Keras. More...
string	WEIGHTS_PATH_NO_TOP = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5'

Detailed Description

Copyright 2017 TensorFlow Authors and Kent Sommer

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.

Function Documentation

def inception_v4.block_inception_a

(

input

)

Definition at line 71 of file inception_v4.py.

def block_inception_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 96, 1, 1)

    branch_1 = conv2d_bn(input, 64, 1, 1)
    branch_1 = conv2d_bn(branch_1, 96, 3, 3)

    branch_2 = conv2d_bn(input, 64, 1, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3, 3)

    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 96, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x

def inception_v4.block_inception_b

(

input

)

Definition at line 111 of file inception_v4.py.

def block_inception_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 1, 1)

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 1, 7)
    branch_1 = conv2d_bn(branch_1, 256, 7, 1)

    branch_2 = conv2d_bn(input, 192, 1, 1)
    branch_2 = conv2d_bn(branch_2, 192, 7, 1)
    branch_2 = conv2d_bn(branch_2, 224, 1, 7)
    branch_2 = conv2d_bn(branch_2, 224, 7, 1)
    branch_2 = conv2d_bn(branch_2, 256, 1, 7)

    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 128, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x

def inception_v4.block_inception_c

(

input

)

Definition at line 156 of file inception_v4.py.

def block_inception_c(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 256, 1, 1)

    branch_1 = conv2d_bn(input, 384, 1, 1)
    branch_10 = conv2d_bn(branch_1, 256, 1, 3)
    branch_11 = conv2d_bn(branch_1, 256, 3, 1)
    branch_1 = concatenate([branch_10, branch_11], axis=channel_axis)


    branch_2 = conv2d_bn(input, 384, 1, 1)
    branch_2 = conv2d_bn(branch_2, 448, 3, 1)
    branch_2 = conv2d_bn(branch_2, 512, 1, 3)
    branch_20 = conv2d_bn(branch_2, 256, 1, 3)
    branch_21 = conv2d_bn(branch_2, 256, 3, 1)
    branch_2 = concatenate([branch_20, branch_21], axis=channel_axis)

    branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
    branch_3 = conv2d_bn(branch_3, 256, 1, 1)

    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
    return x

def inception_v4.block_reduction_a

(

input

)

Definition at line 93 of file inception_v4.py.

def block_reduction_a(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 384, 3, 3, strides=(2,2), padding='valid')

    branch_1 = conv2d_bn(input, 192, 1, 1)
    branch_1 = conv2d_bn(branch_1, 224, 3, 3)
    branch_1 = conv2d_bn(branch_1, 256, 3, 3, strides=(2,2), padding='valid')

    branch_2 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x

def inception_v4.block_reduction_b

(

input

)

Definition at line 136 of file inception_v4.py.

def block_reduction_b(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    branch_0 = conv2d_bn(input, 192, 1, 1)
    branch_0 = conv2d_bn(branch_0, 192, 3, 3, strides=(2, 2), padding='valid')

    branch_1 = conv2d_bn(input, 256, 1, 1)
    branch_1 = conv2d_bn(branch_1, 256, 1, 7)
    branch_1 = conv2d_bn(branch_1, 320, 7, 1)
    branch_1 = conv2d_bn(branch_1, 320, 3, 3, strides=(2,2), padding='valid')

    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)

    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
    return x

def inception_v4.conv2d_bn	(		x,
			nb_filter,
			num_row,
			num_col,
			padding = 'same',
			strides = (1, 1),
			use_bias = False
	)

Utility function to apply conv + BN. 
(Slightly modified from https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py)

Definition at line 51 of file inception_v4.py.

              padding='same', strides=(1, 1), use_bias=False):
    """
    Utility function to apply conv + BN. 
    (Slightly modified from https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py)
    """
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1
    x = Convolution2D(nb_filter, (num_row, num_col),
                      strides=strides,
                      padding=padding,
                      use_bias=use_bias,
                      kernel_regularizer=regularizers.l2(0.00004),
                      kernel_initializer=initializers.VarianceScaling(scale=2.0, mode='fan_in', distribution='normal', seed=None))(x)
    x = BatchNormalization(axis=channel_axis, momentum=0.9997, scale=False)(x)
    x = Activation('relu')(x)
    return x

def inception_v4.create_model	(		num_classes = 1001,
			dropout_prob = 0.2,
			weights = None,
			include_top = True,
			transfer_learning = None,
			input_shape = None
	)

Definition at line 319 of file inception_v4.py.

def create_model(num_classes=1001, dropout_prob=0.2, weights=None, include_top=True, transfer_learning=None, input_shape=None):
    return inception_v4(num_classes, dropout_prob, weights, input_shape, include_top, transfer_learning)

def inception_v4.inception_v4	(		num_classes,
			dropout_keep_prob,
			weights,
			input_shape,
			include_top,
			transfer_learning = None
	)

Creates the inception v4 network

Args:
    num_classes: number of classes
    dropout_keep_prob: float, the fraction to keep before final layer.

Returns: 
    logits: the logits outputs of the model.

Definition at line 242 of file inception_v4.py.

def inception_v4(num_classes, dropout_keep_prob, weights, input_shape, include_top, transfer_learning=None):
    '''
    Creates the inception v4 network

    Args:
        num_classes: number of classes
        dropout_keep_prob: float, the fraction to keep before final layer.
    
    Returns: 
        logits: the logits outputs of the model.
    '''

    # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
    if K.image_data_format() == 'channels_first':
        inputs = Input((input_shape[2], input_shape[0], input_shape[1]))
    else:
        inputs = Input(input_shape)

    # Make inception base
    x = inception_v4_base(inputs)


    # Final pooling and prediction
    if include_top:
        # 1 x 1 x 1536
        x = AveragePooling2D((8,8), padding='valid')(x)
        x = Dropout(dropout_keep_prob)(x)
        x = Flatten()(x)
        # 1536
        x = Dense(units=num_classes, activation='softmax')(x)

    model = Model(inputs, x, name='inception_v4')

    # load weights
    if weights == 'imagenet':
        if K.image_data_format() == 'channels_first':
            if K.backend() == 'tensorflow':
                warnings.warn('You are using the TensorFlow backend, yet you '
                              'are using the Theano '
                              'image data format convention '
                              '(`image_data_format="channels_first"`). '
                              'For best performance, set '
                              '`image_data_format="channels_last"` in '
                              'your Keras config '
                              'at ~/.keras/keras.json.')
        if include_top:
            weights_path = get_file(
                'inception-v4_weights_tf_dim_ordering_tf_kernels.h5',
                WEIGHTS_PATH,
                cache_subdir='models',
                md5_hash='9fe79d77f793fe874470d84ca6ba4a3b')
        else:
            weights_path = get_file(
                'inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5',
                WEIGHTS_PATH_NO_TOP,
                cache_subdir='models',
                md5_hash='9296b46b5971573064d12e4669110969')
        model.load_weights(weights_path, by_name=True)

    # transfer learning
    if transfer_learning == 'pretraining' or transfer_learning == 'finetuning':
        # 1 x 1 x 1536
        x = AveragePooling2D((8,8), padding='valid')(x)
        x = Dropout(dropout_keep_prob)(x)
        x = Flatten()(x)
        # 1536
        x = Dense(units=num_classes, activation='softmax')(x)  

        model = Model(inputs, x, name='inception_v4')

        if transfer_learning == 'finetuning':
            for layer in model.layers[:-4]:
                layer.trainable = False

    return model

def inception_v4.inception_v4_base

(

input

)

Definition at line 184 of file inception_v4.py.

def inception_v4_base(input):
    if K.image_data_format() == 'channels_first':
        channel_axis = 1
    else:
        channel_axis = -1

    # Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
    net = conv2d_bn(input, 32, 3, 3, strides=(2,2), padding='valid')
    net = conv2d_bn(net, 32, 3, 3, padding='valid')
    net = conv2d_bn(net, 64, 3, 3)

    branch_0 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net)

    branch_1 = conv2d_bn(net, 96, 3, 3, strides=(2,2), padding='valid')

    net = concatenate([branch_0, branch_1], axis=channel_axis)

    branch_0 = conv2d_bn(net, 64, 1, 1)
    branch_0 = conv2d_bn(branch_0, 96, 3, 3, padding='valid')

    branch_1 = conv2d_bn(net, 64, 1, 1)
    branch_1 = conv2d_bn(branch_1, 64, 1, 7)
    branch_1 = conv2d_bn(branch_1, 64, 7, 1)
    branch_1 = conv2d_bn(branch_1, 96, 3, 3, padding='valid')

    net = concatenate([branch_0, branch_1], axis=channel_axis)

    branch_0 = conv2d_bn(net, 192, 3, 3, strides=(2,2), padding='valid')
    branch_1 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net)

    net = concatenate([branch_0, branch_1], axis=channel_axis)

    # 35 x 35 x 384
    # 4 x Inception-A blocks
    for idx in range(4):
        net = block_inception_a(net)

    # 35 x 35 x 384
    # Reduction-A block
    net = block_reduction_a(net)

    # 17 x 17 x 1024
    # 7 x Inception-B blocks
    for idx in range(7):
        net = block_inception_b(net)

    # 17 x 17 x 1024
    # Reduction-B block
    net = block_reduction_b(net)

    # 8 x 8 x 1536
    # 3 x Inception-C blocks
    for idx in range(3):
        net = block_inception_c(net)

    return net

def inception_v4.preprocess_input

(

x

)

Definition at line 43 of file inception_v4.py.

def preprocess_input(x):
    x = np.divide(x, 255.0)
    x = np.subtract(x, 0.5)
    x = np.multiply(x, 2.0)
    return x

Variable Documentation

string inception_v4.WEIGHTS_PATH = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels.h5'

Implements the Inception Network v4 (http://arxiv.org/pdf/1602.07261v1.pdf) in Keras.

#

Definition at line 39 of file inception_v4.py.

string inception_v4.WEIGHTS_PATH_NO_TOP = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5'

Definition at line 40 of file inception_v4.py.

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