def build(self, input_shape=None, num_outputs=1000):
"""
Args:
input_shape: The input shape in the form (nb_rows, nb_cols, nb_channels) TensorFlow Format!!
num_outputs: The number of outputs at final softmax layer
Returns:
A compile Keras model.
"""
if len(input_shape) != 3:
raise Exception("Input shape should be a tuple like (nb_rows, nb_cols, nb_channels)")
# (224, 224, 3)
input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=197,
data_format=K.image_data_format(), include_top=True)
img_input = Input(shape=input_shape)
# x = ZeroPadding2D((3, 3))(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(img_input)
# (122, 122, 64)
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool1')(x)
# (56, 56, 64)
x = Conv2D(192, (3, 3), strides=(1, 1), name='conv2')(x)
# (56, 56, 192)
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool2')(x)
# (28, 28, 192)
# * Inception 3a filters=256
# * Inception 3b filters=480
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool3')(x)
# (14, 14, 480)
# * Inception 4a filters=512
# * Inception 4b filters=512
# * Inception 4c filters=512
# * Inception 4d filters=528
# * Inception 4e filters=832
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool4')(x)
# (7, 7, 832)
# * Inception 5a filters=832
# * Inception 5b filters=1024
x = AveragePooling2D(pool_size=(7, 7), strides=(1, 1), padding='same', name='pool5')(x)
# (1, 1, 1024)
x = Dropout(0.4)(x)
x = Dense(units=num_outputs)(x)
x = Activation('softmax')(x)
self.model = Model(inputs=img_input, outputs=x, name='GoogLeNet Model')
return self.model
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