def build(self):
if K.image_data_format() == 'channels_first':
input_shape = (self.img_c, self.frames_n, self.img_w, self.img_h)
else:
input_shape = (self.frames_n, self.img_w, self.img_h, self.img_c)
self.input_data = Input(name='the_input',
shape=input_shape,
dtype='float32')
self.zero1 = ZeroPadding3D(padding=(1, 2, 2),
name='zero1')(self.input_data)
self.conv1 = Conv3D(32, (3, 5, 5),
strides=(1, 2, 2),
kernel_initializer='he_normal',
name='conv1')(self.zero1)
self.batc1 = BatchNormalization(name='batc1')(self.conv1)
self.actv1 = Activation('relu', name='actv1')(self.batc1)
self.drop1 = SpatialDropout3D(0.5)(self.actv1)
self.maxp1 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max1')(self.drop1)
self.zero2 = ZeroPadding3D(padding=(1, 2, 2), name='zero2')(self.maxp1)
self.conv2 = Conv3D(64, (3, 5, 5),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv2')(self.zero2)
self.batc2 = BatchNormalization(name='batc2')(self.conv2)
self.actv2 = Activation('relu', name='actv2')(self.batc2)
self.drop2 = SpatialDropout3D(0.5)(self.actv2)
self.maxp2 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max2')(self.drop2)
self.zero3 = ZeroPadding3D(padding=(1, 1, 1), name='zero3')(self.maxp2)
self.conv3 = Conv3D(96, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv3')(self.zero3)
self.batc3 = BatchNormalization(name='batc3')(self.conv3)
self.actv3 = Activation('relu', name='actv3')(self.batc3)
self.drop3 = SpatialDropout3D(0.5)(self.actv3)
self.maxp3 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max3')(self.drop3)
self.resh1 = TimeDistributed(Flatten())(self.maxp3)
self.gru_1 = Bidirectional(GRU(256,
return_sequences=True,
kernel_initializer='Orthogonal',
name='gru1'),
merge_mode='concat')(self.resh1)
self.gru_2 = Bidirectional(GRU(256,
return_sequences=True,
kernel_initializer='Orthogonal',
name='gru2'),
merge_mode='concat')(self.gru_1)
# transforms RNN output to character activations:
self.dense1 = Dense(self.output_size,
kernel_initializer='he_normal',
name='dense1')(self.gru_2)
self.y_pred = Activation('softmax', name='softmax')(self.dense1)
self.labels = Input(name='the_labels',
shape=[self.absolute_max_string_len],
dtype='float32')
self.input_length = Input(name='input_length',
shape=[1],
dtype='int64')
self.label_length = Input(name='label_length',
shape=[1],
dtype='int64')
self.loss_out = CTC(
'ctc',
[self.y_pred, self.labels, self.input_length, self.label_length])
self.model = Model(inputs=[
self.input_data, self.labels, self.input_length, self.label_length
],
outputs=self.loss_out)
def build(self):
if K.image_data_format() == 'channels_first':
input_shape = (self.img_c, self.frames_n, self.img_w, self.img_h)
else:
input_shape = (self.frames_n, self.img_w, self.img_h, self.img_c)
self.input_data = Input(name='the_input',
shape=input_shape,
dtype='float32')
self.zero1 = ZeroPadding3D(padding=(1, 0, 0),
name='zero1')(self.input_data)
self.conv1 = Conv3D(64, (3, 3, 3),
strides=(1, 2, 2),
kernel_initializer='he_normal',
name='conv1')(self.zero1)
self.gn1 = GroupNormalization(name='gn1', groups=32)(self.conv1)
self.actv1 = Activation('relu', name='actv1')(self.gn1)
self.maxp1 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max1')(self.actv1)
self.zero2 = ZeroPadding3D(padding=(1, 0, 0), name='zero2')(self.maxp1)
self.conv2 = Conv3D(128, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv2')(self.zero2)
self.gn2 = GroupNormalization(name='gn2', groups=32)(self.conv2)
self.actv2 = Activation('relu', name='actv2')(self.gn2)
self.maxp2 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max2')(self.actv2)
self.zero3 = ZeroPadding3D(padding=(1, 0, 0), name='zero3')(self.maxp2)
self.conv3 = Conv3D(256, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv3')(self.zero3)
self.gn3 = GroupNormalization(name='gn3', groups=32)(self.conv3)
self.actv3 = Activation('relu', name='actv3')(self.gn3)
self.maxp3 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 2, 2),
name='max3')(self.actv3)
self.zero4 = ZeroPadding3D(padding=(1, 0, 0), name='zero4')(self.maxp3)
self.conv4 = Conv3D(512, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv4')(self.zero4)
self.gn4 = GroupNormalization(name='gn4', groups=32)(self.conv4)
self.actv4 = Activation('relu', name='actv4')(self.gn4)
self.zero5 = ZeroPadding3D(padding=(1, 0, 0), name='zero5')(self.actv4)
self.conv5 = Conv3D(512, (3, 3, 3),
strides=(1, 1, 1),
kernel_initializer='he_normal',
name='conv5')(self.zero5)
self.gn5 = GroupNormalization(name='gn5', groups=32)(self.conv5)
self.actv5 = Activation('relu', name='actv5')(self.gn5)
self.maxp5 = MaxPooling3D(pool_size=(1, 2, 2),
strides=(1, 1, 1),
name='max5')(self.actv5)
self.resh1 = TimeDistributed(Flatten())(self.maxp5)
self.lstm_1 = Bidirectional(LSTM(768,
return_sequences=True,
kernel_initializer='Orthogonal',
name='lstm1'),
merge_mode='concat')(self.resh1)
self.lstm_1_gn = GroupNormalization(name='lstm_1_gn',
groups=32)(self.lstm_1)
self.lstm_2 = Bidirectional(LSTM(768,
return_sequences=True,
kernel_initializer='Orthogonal',
name='lstm2'),
merge_mode='concat')(self.lstm_1_gn)
self.lstm_2_gn = GroupNormalization(name='lstm_2_gn',
groups=32)(self.lstm_2)
self.lstm_3 = Bidirectional(LSTM(768,
return_sequences=True,
kernel_initializer='Orthogonal',
name='lstm3'),
merge_mode='concat')(self.lstm_2_gn)
self.lstm_3_gn = GroupNormalization(name='lstm_3_gn',
groups=32)(self.lstm_3)
# transforms RNN output to character activations:
self.dense1 = Dense(768, kernel_initializer='he_normal',
name='dense1')(self.lstm_3_gn)
self.gn6 = GroupNormalization(name='gn6', groups=32)(self.dense1)
self.actv6 = Activation('relu', name='actv6')(self.gn6)
self.dense2 = Dense(self.output_size,
kernel_initializer='he_normal',
name='dense2')(self.actv6)
self.y_pred = Activation('softmax', name='softmax')(self.dense2)
self.labels = Input(name='the_labels',
shape=[self.absolute_max_string_len],
dtype='float32')
self.input_length = Input(name='input_length',
shape=[1],
dtype='int64')
self.label_length = Input(name='label_length',
shape=[1],
dtype='int64')
self.loss_out = CTC(
'ctc',
[self.y_pred, self.labels, self.input_length, self.label_length])
self.model = Model(inputs=[
self.input_data, self.labels, self.input_length, self.label_length
],
outputs=self.loss_out)