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amansrivastava17/googlenet.py

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Created July 31, 2018 11:54
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  1. @joelouismarino joelouismarino revised this gist Jun 24, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion googlenet.py
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    @@ -1,6 +1,6 @@
    from scipy.misc import imread, imresize

    from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, BatchNormalization, Dropout, Flatten, Lambda, merge, Reshape, Activation
    from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
    from keras.models import Model
    from keras.regularizers import l2
    from keras.optimizers import SGD
  2. @joelouismarino joelouismarino revised this gist Jun 24, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion readme.md
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    @@ -12,7 +12,7 @@ GoogLeNet paper:

    ###Contents:

    Custom layers for GoogLeNet: `googlenetcustom_layers.py`
    Custom layers for GoogLeNet: `googlenet_custom_layers.py`
    Model and usage demo: `googlenet.py`

    Weights: [googlenet_weights.h5](https://drive.google.com/open?id=0B319laiAPjU3RE1maU9MMlh2dnc)
  3. @joelouismarino joelouismarino revised this gist Jun 24, 2016. 1 changed file with 2 additions and 2 deletions.
    4 changes: 2 additions & 2 deletions readme.md
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    @@ -12,8 +12,8 @@ GoogLeNet paper:

    ###Contents:

    Custom layers for GoogLeNet: `custom_layers.py`
    Model and usage demo: see `googlenet.py`
    Custom layers for GoogLeNet: `googlenetcustom_layers.py`
    Model and usage demo: `googlenet.py`

    Weights: [googlenet_weights.h5](https://drive.google.com/open?id=0B319laiAPjU3RE1maU9MMlh2dnc)

  4. @joelouismarino joelouismarino revised this gist Jun 24, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion googlenet.py
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    def create_googlenet(weights_path=None):
    # creates GoogLeNet a.k.a. Inception v1 (Szegedy, 2014)
    # creates GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)

    input = Input(shape=(3, 224, 224))

  5. @joelouismarino joelouismarino revised this gist Jun 24, 2016. 2 changed files with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion googlenet.py
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    @@ -4,7 +4,7 @@
    from keras.models import Model
    from keras.regularizers import l2
    from keras.optimizers import SGD
    from custom_layers import PoolHelper,LRN
    from googlenet_custom_layers import PoolHelper,LRN


    def create_googlenet(weights_path=None):
    0 custom_layers.py → googlenet_custom_layers.py
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    File renamed without changes.
  6. @joelouismarino joelouismarino revised this gist Jun 24, 2016. No changes.
  7. @joelouismarino joelouismarino created this gist Jun 24, 2016.
    48 changes: 48 additions & 0 deletions custom_layers.py
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    from keras.layers.core import Layer
    import theano.tensor as T

    class LRN(Layer):

    def __init__(self, alpha=0.0001,k=1,beta=0.75,n=5, **kwargs):
    self.alpha = alpha
    self.k = k
    self.beta = beta
    self.n = n
    super(LRN, self).__init__(**kwargs)

    def call(self, x, mask=None):
    b, ch, r, c = x.shape
    half_n = self.n // 2 # half the local region
    input_sqr = T.sqr(x) # square the input
    extra_channels = T.alloc(0., b, ch + 2*half_n, r, c) # make an empty tensor with zero pads along channel dimension
    input_sqr = T.set_subtensor(extra_channels[:, half_n:half_n+ch, :, :],input_sqr) # set the center to be the squared input
    scale = self.k # offset for the scale
    norm_alpha = self.alpha / self.n # normalized alpha
    for i in range(self.n):
    scale += norm_alpha * input_sqr[:, i:i+ch, :, :]
    scale = scale ** self.beta
    x = x / scale
    return x

    def get_config(self):
    config = {"alpha": self.alpha,
    "k": self.k,
    "beta": self.beta,
    "n": self.n}
    base_config = super(LRN, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


    class PoolHelper(Layer):

    def __init__(self, **kwargs):
    super(PoolHelper, self).__init__(**kwargs)

    def call(self, x, mask=None):
    return x[:,:,1:,1:]

    def get_config(self):
    config = {}
    base_config = super(PoolHelper, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

    269 changes: 269 additions & 0 deletions googlenet.py
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    from scipy.misc import imread, imresize

    from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, BatchNormalization, Dropout, Flatten, Lambda, merge, Reshape, Activation
    from keras.models import Model
    from keras.regularizers import l2
    from keras.optimizers import SGD
    from custom_layers import PoolHelper,LRN


    def create_googlenet(weights_path=None):
    # creates GoogLeNet a.k.a. Inception v1 (Szegedy, 2014)

    input = Input(shape=(3, 224, 224))

    conv1_7x7_s2 = Convolution2D(64,7,7,subsample=(2,2),border_mode='same',activation='relu',name='conv1/7x7_s2',W_regularizer=l2(0.0002))(input)

    conv1_zero_pad = ZeroPadding2D(padding=(1, 1))(conv1_7x7_s2)

    pool1_helper = PoolHelper()(conv1_zero_pad)

    pool1_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool1/3x3_s2')(pool1_helper)

    pool1_norm1 = LRN(name='pool1/norm1')(pool1_3x3_s2)

    conv2_3x3_reduce = Convolution2D(64,1,1,border_mode='same',activation='relu',name='conv2/3x3_reduce',W_regularizer=l2(0.0002))(pool1_norm1)

    conv2_3x3 = Convolution2D(192,3,3,border_mode='same',activation='relu',name='conv2/3x3',W_regularizer=l2(0.0002))(conv2_3x3_reduce)

    conv2_norm2 = LRN(name='conv2/norm2')(conv2_3x3)

    conv2_zero_pad = ZeroPadding2D(padding=(1, 1))(conv2_norm2)

    pool2_helper = PoolHelper()(conv2_zero_pad)

    pool2_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool2/3x3_s2')(pool2_helper)


    inception_3a_1x1 = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_3a/1x1',W_regularizer=l2(0.0002))(pool2_3x3_s2)

    inception_3a_3x3_reduce = Convolution2D(96,1,1,border_mode='same',activation='relu',name='inception_3a/3x3_reduce',W_regularizer=l2(0.0002))(pool2_3x3_s2)

    inception_3a_3x3 = Convolution2D(128,3,3,border_mode='same',activation='relu',name='inception_3a/3x3',W_regularizer=l2(0.0002))(inception_3a_3x3_reduce)

    inception_3a_5x5_reduce = Convolution2D(16,1,1,border_mode='same',activation='relu',name='inception_3a/5x5_reduce',W_regularizer=l2(0.0002))(pool2_3x3_s2)

    inception_3a_5x5 = Convolution2D(32,5,5,border_mode='same',activation='relu',name='inception_3a/5x5',W_regularizer=l2(0.0002))(inception_3a_5x5_reduce)

    inception_3a_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_3a/pool')(pool2_3x3_s2)

    inception_3a_pool_proj = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_3a/pool_proj',W_regularizer=l2(0.0002))(inception_3a_pool)

    inception_3a_output = merge([inception_3a_1x1,inception_3a_3x3,inception_3a_5x5,inception_3a_pool_proj],mode='concat',concat_axis=1,name='inception_3a/output')


    inception_3b_1x1 = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_3b/1x1',W_regularizer=l2(0.0002))(inception_3a_output)

    inception_3b_3x3_reduce = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_3b/3x3_reduce',W_regularizer=l2(0.0002))(inception_3a_output)

    inception_3b_3x3 = Convolution2D(192,3,3,border_mode='same',activation='relu',name='inception_3b/3x3',W_regularizer=l2(0.0002))(inception_3b_3x3_reduce)

    inception_3b_5x5_reduce = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_3b/5x5_reduce',W_regularizer=l2(0.0002))(inception_3a_output)

    inception_3b_5x5 = Convolution2D(96,5,5,border_mode='same',activation='relu',name='inception_3b/5x5',W_regularizer=l2(0.0002))(inception_3b_5x5_reduce)

    inception_3b_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_3b/pool')(inception_3a_output)

    inception_3b_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_3b/pool_proj',W_regularizer=l2(0.0002))(inception_3b_pool)

    inception_3b_output = merge([inception_3b_1x1,inception_3b_3x3,inception_3b_5x5,inception_3b_pool_proj],mode='concat',concat_axis=1,name='inception_3b/output')


    inception_3b_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_output)

    pool3_helper = PoolHelper()(inception_3b_output_zero_pad)

    pool3_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool3/3x3_s2')(pool3_helper)


    inception_4a_1x1 = Convolution2D(192,1,1,border_mode='same',activation='relu',name='inception_4a/1x1',W_regularizer=l2(0.0002))(pool3_3x3_s2)

    inception_4a_3x3_reduce = Convolution2D(96,1,1,border_mode='same',activation='relu',name='inception_4a/3x3_reduce',W_regularizer=l2(0.0002))(pool3_3x3_s2)

    inception_4a_3x3 = Convolution2D(208,3,3,border_mode='same',activation='relu',name='inception_4a/3x3',W_regularizer=l2(0.0002))(inception_4a_3x3_reduce)

    inception_4a_5x5_reduce = Convolution2D(16,1,1,border_mode='same',activation='relu',name='inception_4a/5x5_reduce',W_regularizer=l2(0.0002))(pool3_3x3_s2)

    inception_4a_5x5 = Convolution2D(48,5,5,border_mode='same',activation='relu',name='inception_4a/5x5',W_regularizer=l2(0.0002))(inception_4a_5x5_reduce)

    inception_4a_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4a/pool')(pool3_3x3_s2)

    inception_4a_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_4a/pool_proj',W_regularizer=l2(0.0002))(inception_4a_pool)

    inception_4a_output = merge([inception_4a_1x1,inception_4a_3x3,inception_4a_5x5,inception_4a_pool_proj],mode='concat',concat_axis=1,name='inception_4a/output')


    loss1_ave_pool = AveragePooling2D(pool_size=(5,5),strides=(3,3),name='loss1/ave_pool')(inception_4a_output)

    loss1_conv = Convolution2D(128,1,1,border_mode='same',activation='relu',name='loss1/conv',W_regularizer=l2(0.0002))(loss1_ave_pool)

    loss1_flat = Flatten()(loss1_conv)

    loss1_fc = Dense(1024,activation='relu',name='loss1/fc',W_regularizer=l2(0.0002))(loss1_flat)

    loss1_drop_fc = Dropout(0.7)(loss1_fc)

    loss1_classifier = Dense(1000,name='loss1/classifier',W_regularizer=l2(0.0002))(loss1_drop_fc)

    loss1_classifier_act = Activation('softmax')(loss1_classifier)


    inception_4b_1x1 = Convolution2D(160,1,1,border_mode='same',activation='relu',name='inception_4b/1x1',W_regularizer=l2(0.0002))(inception_4a_output)

    inception_4b_3x3_reduce = Convolution2D(112,1,1,border_mode='same',activation='relu',name='inception_4b/3x3_reduce',W_regularizer=l2(0.0002))(inception_4a_output)

    inception_4b_3x3 = Convolution2D(224,3,3,border_mode='same',activation='relu',name='inception_4b/3x3',W_regularizer=l2(0.0002))(inception_4b_3x3_reduce)

    inception_4b_5x5_reduce = Convolution2D(24,1,1,border_mode='same',activation='relu',name='inception_4b/5x5_reduce',W_regularizer=l2(0.0002))(inception_4a_output)

    inception_4b_5x5 = Convolution2D(64,5,5,border_mode='same',activation='relu',name='inception_4b/5x5',W_regularizer=l2(0.0002))(inception_4b_5x5_reduce)

    inception_4b_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4b/pool')(inception_4a_output)

    inception_4b_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_4b/pool_proj',W_regularizer=l2(0.0002))(inception_4b_pool)

    inception_4b_output = merge([inception_4b_1x1,inception_4b_3x3,inception_4b_5x5,inception_4b_pool_proj],mode='concat',concat_axis=1,name='inception_4b_output')


    inception_4c_1x1 = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_4c/1x1',W_regularizer=l2(0.0002))(inception_4b_output)

    inception_4c_3x3_reduce = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_4c/3x3_reduce',W_regularizer=l2(0.0002))(inception_4b_output)

    inception_4c_3x3 = Convolution2D(256,3,3,border_mode='same',activation='relu',name='inception_4c/3x3',W_regularizer=l2(0.0002))(inception_4c_3x3_reduce)

    inception_4c_5x5_reduce = Convolution2D(24,1,1,border_mode='same',activation='relu',name='inception_4c/5x5_reduce',W_regularizer=l2(0.0002))(inception_4b_output)

    inception_4c_5x5 = Convolution2D(64,5,5,border_mode='same',activation='relu',name='inception_4c/5x5',W_regularizer=l2(0.0002))(inception_4c_5x5_reduce)

    inception_4c_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4c/pool')(inception_4b_output)

    inception_4c_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_4c/pool_proj',W_regularizer=l2(0.0002))(inception_4c_pool)

    inception_4c_output = merge([inception_4c_1x1,inception_4c_3x3,inception_4c_5x5,inception_4c_pool_proj],mode='concat',concat_axis=1,name='inception_4c/output')


    inception_4d_1x1 = Convolution2D(112,1,1,border_mode='same',activation='relu',name='inception_4d/1x1',W_regularizer=l2(0.0002))(inception_4c_output)

    inception_4d_3x3_reduce = Convolution2D(144,1,1,border_mode='same',activation='relu',name='inception_4d/3x3_reduce',W_regularizer=l2(0.0002))(inception_4c_output)

    inception_4d_3x3 = Convolution2D(288,3,3,border_mode='same',activation='relu',name='inception_4d/3x3',W_regularizer=l2(0.0002))(inception_4d_3x3_reduce)

    inception_4d_5x5_reduce = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_4d/5x5_reduce',W_regularizer=l2(0.0002))(inception_4c_output)

    inception_4d_5x5 = Convolution2D(64,5,5,border_mode='same',activation='relu',name='inception_4d/5x5',W_regularizer=l2(0.0002))(inception_4d_5x5_reduce)

    inception_4d_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4d/pool')(inception_4c_output)

    inception_4d_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_4d/pool_proj',W_regularizer=l2(0.0002))(inception_4d_pool)

    inception_4d_output = merge([inception_4d_1x1,inception_4d_3x3,inception_4d_5x5,inception_4d_pool_proj],mode='concat',concat_axis=1,name='inception_4d/output')


    loss2_ave_pool = AveragePooling2D(pool_size=(5,5),strides=(3,3),name='loss2/ave_pool')(inception_4d_output)

    loss2_conv = Convolution2D(128,1,1,border_mode='same',activation='relu',name='loss2/conv',W_regularizer=l2(0.0002))(loss2_ave_pool)

    loss2_flat = Flatten()(loss2_conv)

    loss2_fc = Dense(1024,activation='relu',name='loss2/fc',W_regularizer=l2(0.0002))(loss2_flat)

    loss2_drop_fc = Dropout(0.7)(loss2_fc)

    loss2_classifier = Dense(1000,name='loss2/classifier',W_regularizer=l2(0.0002))(loss2_drop_fc)

    loss2_classifier_act = Activation('softmax')(loss2_classifier)


    inception_4e_1x1 = Convolution2D(256,1,1,border_mode='same',activation='relu',name='inception_4e/1x1',W_regularizer=l2(0.0002))(inception_4d_output)

    inception_4e_3x3_reduce = Convolution2D(160,1,1,border_mode='same',activation='relu',name='inception_4e/3x3_reduce',W_regularizer=l2(0.0002))(inception_4d_output)

    inception_4e_3x3 = Convolution2D(320,3,3,border_mode='same',activation='relu',name='inception_4e/3x3',W_regularizer=l2(0.0002))(inception_4e_3x3_reduce)

    inception_4e_5x5_reduce = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_4e/5x5_reduce',W_regularizer=l2(0.0002))(inception_4d_output)

    inception_4e_5x5 = Convolution2D(128,5,5,border_mode='same',activation='relu',name='inception_4e/5x5',W_regularizer=l2(0.0002))(inception_4e_5x5_reduce)

    inception_4e_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4e/pool')(inception_4d_output)

    inception_4e_pool_proj = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_4e/pool_proj',W_regularizer=l2(0.0002))(inception_4e_pool)

    inception_4e_output = merge([inception_4e_1x1,inception_4e_3x3,inception_4e_5x5,inception_4e_pool_proj],mode='concat',concat_axis=1,name='inception_4e/output')


    inception_4e_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_4e_output)

    pool4_helper = PoolHelper()(inception_4e_output_zero_pad)

    pool4_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool4/3x3_s2')(pool4_helper)


    inception_5a_1x1 = Convolution2D(256,1,1,border_mode='same',activation='relu',name='inception_5a/1x1',W_regularizer=l2(0.0002))(pool4_3x3_s2)

    inception_5a_3x3_reduce = Convolution2D(160,1,1,border_mode='same',activation='relu',name='inception_5a/3x3_reduce',W_regularizer=l2(0.0002))(pool4_3x3_s2)

    inception_5a_3x3 = Convolution2D(320,3,3,border_mode='same',activation='relu',name='inception_5a/3x3',W_regularizer=l2(0.0002))(inception_5a_3x3_reduce)

    inception_5a_5x5_reduce = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_5a/5x5_reduce',W_regularizer=l2(0.0002))(pool4_3x3_s2)

    inception_5a_5x5 = Convolution2D(128,5,5,border_mode='same',activation='relu',name='inception_5a/5x5',W_regularizer=l2(0.0002))(inception_5a_5x5_reduce)

    inception_5a_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_5a/pool')(pool4_3x3_s2)

    inception_5a_pool_proj = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_5a/pool_proj',W_regularizer=l2(0.0002))(inception_5a_pool)

    inception_5a_output = merge([inception_5a_1x1,inception_5a_3x3,inception_5a_5x5,inception_5a_pool_proj],mode='concat',concat_axis=1,name='inception_5a/output')


    inception_5b_1x1 = Convolution2D(384,1,1,border_mode='same',activation='relu',name='inception_5b/1x1',W_regularizer=l2(0.0002))(inception_5a_output)

    inception_5b_3x3_reduce = Convolution2D(192,1,1,border_mode='same',activation='relu',name='inception_5b/3x3_reduce',W_regularizer=l2(0.0002))(inception_5a_output)

    inception_5b_3x3 = Convolution2D(384,3,3,border_mode='same',activation='relu',name='inception_5b/3x3',W_regularizer=l2(0.0002))(inception_5b_3x3_reduce)

    inception_5b_5x5_reduce = Convolution2D(48,1,1,border_mode='same',activation='relu',name='inception_5b/5x5_reduce',W_regularizer=l2(0.0002))(inception_5a_output)

    inception_5b_5x5 = Convolution2D(128,5,5,border_mode='same',activation='relu',name='inception_5b/5x5',W_regularizer=l2(0.0002))(inception_5b_5x5_reduce)

    inception_5b_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_5b/pool')(inception_5a_output)

    inception_5b_pool_proj = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_5b/pool_proj',W_regularizer=l2(0.0002))(inception_5b_pool)

    inception_5b_output = merge([inception_5b_1x1,inception_5b_3x3,inception_5b_5x5,inception_5b_pool_proj],mode='concat',concat_axis=1,name='inception_5b/output')


    pool5_7x7_s1 = AveragePooling2D(pool_size=(7,7),strides=(1,1),name='pool5/7x7_s2')(inception_5b_output)

    loss3_flat = Flatten()(pool5_7x7_s1)

    pool5_drop_7x7_s1 = Dropout(0.4)(loss3_flat)

    loss3_classifier = Dense(1000,name='loss3/classifier',W_regularizer=l2(0.0002))(pool5_drop_7x7_s1)

    loss3_classifier_act = Activation('softmax',name='prob')(loss3_classifier)


    googlenet = Model(input=input, output=[loss1_classifier_act,loss2_classifier_act,loss3_classifier_act])

    if weights_path:
    googlenet.load_weights(weights_path)

    return googlenet



    if __name__ == "__main__":
    img = imresize(imread('cat.jpg', mode='RGB'), (224, 224)).astype(np.float32)
    img[:, :, 0] -= 123.68
    img[:, :, 1] -= 116.779
    img[:, :, 2] -= 103.939
    img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
    img = img.transpose((2, 0, 1))
    img = np.expand_dims(img, axis=0)

    # Test pretrained model
    model = create_googlenet('googlenet_weights.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(img) # note: the model has three outputs
    print np.argmax(out[2])
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    ## GoogLeNet in Keras

    Here is a [Keras](http://keras.io/) model of GoogLeNet (a.k.a Inception V1). I created it by converting the GoogLeNet model from Caffe.
    For more details on the conversion, see [here](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html).

    GoogLeNet paper:

    Going deeper with convolutions.
    Szegedy, Christian, et al.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.


    ###Contents:

    Custom layers for GoogLeNet: `custom_layers.py`
    Model and usage demo: see `googlenet.py`

    Weights: [googlenet_weights.h5](https://drive.google.com/open?id=0B319laiAPjU3RE1maU9MMlh2dnc)