def attention_block_2d(x, g, inter_channel):
'''
:param x: x input from down_sampling same layer output (?,x_height,x_width,x_channel)
:param g: gate input from up_sampling layer last output (?,g_height,g_width,g_channel)
g_height,g_width=x_height/2,x_width/2
:return:
'''
print('attention_block:')
# theta_x(?,g_height,g_width,inter_channel)
theta_x = Conv2D(inter_channel, [2, 2], strides=[2, 2])(x)
print(str(theta_x.get_shape()))
# phi_g(?,g_height,g_width,inter_channel)
phi_g = Conv2D(inter_channel, [1, 1], strides=[1, 1])(g)
print(str(phi_g.get_shape()))
# f(?,g_height,g_width,inter_channel)
f = Activation('relu')(keras.layers.add([theta_x, phi_g]))
print(str(f.get_shape()))
# psi_f(?,g_height,g_width,1)
psi_f = Conv2D(1, [1, 1], strides=[1, 1])(f)
print(str(psi_f.get_shape()))
# sigm_psi_f(?,g_height,g_width)
sigm_psi_f = Activation('sigmoid')(psi_f)
print(str(sigm_psi_f.get_shape()))
# rate(?,x_height,x_width)
rate = UpSampling2D(size=[2, 2])(sigm_psi_f)
print(str(rate.get_shape()))
# att_x(?,x_height,x_width,x_channel)
att_x = keras.layers.multiply([x, rate])
print(str(att_x.get_shape()))
print('-----------------')
return att_x
def _add_dense_layers(self, tensor_list, layer_count=1, activation="relu",
dimension_multiplier: float = 1, out_dim: int = None, regularization=None, names=None):
for layer_index in range(layer_count):
new_list = []
for i, item in enumerate(tensor_list):
name = None
if names is not None:
name = names(layer_index, i)
item = Activation(activation)(TimeDistributed(name=name,
layer=Dense(out_dim if out_dim is not None else int(
int(item.get_shape()[-1]) * dimension_multiplier),
kernel_regularizer=regularization,
bias_regularizer=regularization))(item))
new_list.append(item)
tensor_list = new_list
return tensor_list
def unet_model_2d_attention(input_shape,
n_labels,
batch_normalization=False,
initial_learning_rate=0.00001,
metrics=m.MIoU):
"""
input_shape:without batch_size,(img_height,img_width,img_depth)
metrics:
"""
inputs = Input(input_shape)
down_layer = []
layer = inputs
#down_layer_1
layer = res_block_v2(layer, 64, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling2D(pool_size=[2, 2], strides=[2, 2])(layer)
print(str(layer.get_shape()))
# down_layer_2
layer = res_block_v2(layer, 128, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling2D(pool_size=[2, 2], strides=[2, 2])(layer)
print(str(layer.get_shape()))
# down_layer_3
layer = res_block_v2(layer, 256, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling2D(pool_size=[2, 2], strides=[2, 2])(layer)
print(str(layer.get_shape()))
# down_layer_4
layer = res_block_v2(layer, 512, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling2D(pool_size=[2, 2], strides=[2, 2])(layer)
print(str(layer.get_shape()))
# bottle_layer
layer = res_block_v2(layer, 1024, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_4
layer = attention_block_2d(down_layer[3], layer, 256)
layer = res_block_v2(layer, 512, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_3
layer = attention_block_2d(down_layer[2], layer, 128)
layer = res_block_v2(layer, 256, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_2
layer = attention_block_2d(down_layer[1], layer, 64)
layer = res_block_v2(layer, 128, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_1
layer = attention_block_2d(down_layer[0], layer, 32)
layer = res_block_v2(layer, 64, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# score_layer
layer = Conv2D(n_labels, [1, 1], strides=[1, 1])(layer)
print(str(layer.get_shape()))
# softmax
layer = Activation('softmax')(layer)
print(str(layer.get_shape()))
outputs = layer
model = Model(inputs=inputs, outputs=outputs)
metrics = [metrics]
model.compile(optimizer=Adam(lr=initial_learning_rate),
loss=m.MIoU_loss,
metrics=metrics)
return model
# Small path
small_window_input = Input(shape=input_shape, name="small_windows")
merged_inputs = merge([big_window_relu1, small_window_input], mode='concat')
# Local path
kernel_size = (7, 7)
nb_filters = 64
print("Input shape to the network:", merged_inputs.get_shape())
local_conv1 = Convolution2D(nb_filters,
kernel_size[0],
kernel_size[1],
border_mode='valid')(merged_inputs)
print("Output shape of 1st convolution:", local_conv1.get_shape())
local_relu1 = Activation('relu')(local_conv1)
print("Output shape of relu:", local_relu1.get_shape())
local_maxpool1 = MaxPooling2D(pool_size=pool_size, strides=(1, 1))(local_relu1)
#local_dropout1 = Dropout(0.25)(local_maxpool1)
print("Output shape of max pooling:", local_maxpool1.get_shape())
kernel_size = (3, 3)
nb_filters = 64
pool_size = (2, 2)
local_conv2 = Convolution2D(nb_filters,
kernel_size[0],
kernel_size[1],
border_mode='valid',
input_shape=input_shape)(local_maxpool1)
print("Output shape of 2nd convolution:", local_conv2.get_shape())
local_relu2 = Activation('relu')(local_conv2)
print("Output shape of 2nd relu:", local_relu2.get_shape())
import keras
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.layers import Input, Merge, Dense, Lambda, Activation
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution1D
def attentive(q, a):
return tf.batch_matmul(q, a, adj_x=False, adj_y=True, name="attention")
q = Input(shape=[None,10], dtype='float32')
a = Input(shape=[None,10], dtype='float32')
qua = Activation('tanh')(
Merge(
mode=lambda x: attentive(*x),
output_shape=lambda x: x[0][0:2] + x[1][2:]
)([q, a])
)
print qua.get_shape()
q_softmax = Activation('softmax')(Lambda(lambda x: K.max(x, axis=2, keepdims=True))(qua))
print q_softmax.get_shape()
a_softmax = Activation('softmax')(Lambda(lambda x: K.max(x, axis=1, keepdims=True))(qua))
print a_softmax.get_shape()
sess = tf.Session()
print sess.run([q_softmax], {q:np.random.random((3,5,10)), a:np.random.random((3,7,10))})[0].shape
print sess.run([a_softmax], {q:np.random.random((3,5,10)), a:np.random.random((3,7,10))})[0].shape
def relu_deep_model1(input_shape, relu_max):
input_img = Input(shape=input_shape)
print 'input shape:', input_img._keras_shape
# 32, 32
x = Conv2D(64,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(input_img)
x = BatchNormalization(mode=2, axis=3)(x)
# 16, 16
x = Conv2D(128,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 8, 8
x = Conv2D(256,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 4, 4
# latent_dim = (1, 1, 1024)
x = Conv2D(1024,
4,
4,
activation='linear',
border_mode='same',
subsample=(4, 4))(x)
x = GaussianNoise(0.2)(x)
# encoded = Activation('relu')(x)
encoded = Activation(relu_n(relu_max))(x)
print 'encoded shape:', encoded.get_shape().as_list()
# in the origianl design, no BN as the first layer of decoder because of bug
x = encoded
# x = BatchNormalization(mode=2, axis=3)(encoded)
# batch_size, h, w, _ = tf.shape(x)
batch_size = tf.shape(x)[0]
# dim: (1, 1, 512)
x = Deconv2D(512,
4,
4,
output_shape=[batch_size, 4, 4, 512],
activation='relu',
border_mode='same',
subsample=(4, 4))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# (4, 4, 512)
x = Deconv2D(256,
5,
5,
output_shape=[batch_size, 8, 8, 256],
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (8, 8, 256)
x = Deconv2D(128,
5,
5,
output_shape=(batch_size, 16, 16, 128),
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (16, 16, 256)
x = Deconv2D(64,
5,
5,
output_shape=(batch_size, 32, 32, 64),
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (32, 32, 64)
x = Deconv2D(3,
5,
5,
output_shape=(batch_size, 32, 32, 3),
activation='linear',
border_mode='same',
subsample=(1, 1))(x)
decoded = BatchNormalization(mode=2, axis=3)(x)
print 'decoded shape:', decoded.get_shape().as_list()
autoencoder = Model(input_img, decoded)
return autoencoder
def deep_model2(input_shape):
input_img = Input(shape=input_shape)
print 'input shape:', input_img._keras_shape
# 32, 32
x = Conv2D(32,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(input_img)
x = BatchNormalization(mode=2, axis=3)(x)
# 16, 16
x = Conv2D(64,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 8, 8
x = Conv2D(128,
3,
3,
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# 4, 4
# latent_dim = (1, 1, 1024)
x = Conv2D(1024,
4,
4,
activation='linear',
border_mode='same',
subsample=(4, 4))(x)
x = GaussianNoise(0.1)(x)
encoded = Activation('sigmoid')(x)
print 'encoded shape:', encoded.get_shape().as_list()
# x = BatchNormalization(mode=2, axis=3)(encoded)
# batch_size, h, w, _ = tf.shape(x)
batch_size = tf.shape(encoded)[0]
# dim: (1, 1, 512)
x = Deconv2D(512,
4,
4,
output_shape=[batch_size, 4, 4, 512],
activation='relu',
border_mode='same',
subsample=(4, 4))(encoded)
x = BatchNormalization(mode=2, axis=3)(x)
# (4, 4, 512)
x = Deconv2D(256,
5,
5,
output_shape=[batch_size, 8, 8, 256],
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (8, 8, 256)
x = Deconv2D(128,
5,
5,
output_shape=(batch_size, 16, 16, 128),
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (16, 16, 256)
x = Deconv2D(64,
5,
5,
output_shape=(batch_size, 32, 32, 64),
activation='relu',
border_mode='same',
subsample=(2, 2))(x)
x = BatchNormalization(mode=2, axis=3)(x)
# dim: (32, 32, 64)
x = Deconv2D(3,
5,
5,
output_shape=(batch_size, 32, 32, 3),
activation='linear',
border_mode='same',
subsample=(1, 1))(x)
decoded = BatchNormalization(mode=2, axis=3)(x)
print 'decoded shape:', decoded.get_shape().as_list()
autoencoder = Model(input_img, decoded)
return autoencoder
def model():
# Unet
input_shape = (82, 1)
input_data = Input(shape=input_shape)
x = Conv1D(2, kernel_size=3, padding='same')(input_data)
#Rajouter une couche BN
x = Activation('relu')(x)
print("in --> Conv1D(32, 2, same) = {}".format(
x.get_shape())) # (?, 81, 32)
x = Conv1D(4, kernel_size=2, padding='same')(input_data)
x = Activation('relu')(x)
print("Conv1D --> Conv1D(32, 2, same). Out_shape = {} \n".format(
x.get_shape()))
#print x.get_shape() # (?, 81, 32)
in_pool_shape = x.get_shape()
x = MaxPool1D(pool_size=2, padding='same')(x)
print("MaxPool1D pool_size = 2, padding same")
print("MaxPool1D(1st enc): in_shape = {}\tOut_shape = {}".format(
in_pool_shape, x.get_shape()))
print(" ")
#
x = Conv1D(8, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Pooled --> Conv1D(64, 2, same). Out_shape = {}".format(
x.get_shape()))
x = Conv1D(64, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Conv1D --> Conv1D(64, 2, same). Out_shape = {} \n".format(
x.get_shape()))
in_pool_shape = x.get_shape()
x = MaxPool1D(pool_size=2, padding='same')(x)
print("MaxPool1D pool_size = 2, padding same")
print("MaxPool1D(2nd enc): in_shape = {}\tOut_shape = {}".format(
in_pool_shape, x.get_shape()))
print(" ")
x = Conv1D(16, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Pooled --> Conv1D(128, 2, same). Out_shape = {}".format(
x.get_shape()))
x = Conv1D(16, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Conv1D --> Conv1D(128, 2, same). Out_shape = {} \n".format(
x.get_shape()))
in_samp_shape = x.get_shape()
x = UpSampling1D(2)(x)
print("Upsampling pool_size = 2")
print("UpSampling(1st dec): in_shape= {}\tOut_shape = {}".format(
in_samp_shape, x.get_shape()))
print(" ")
x = Conv1D(8, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Upsampled --> Conv1D(64, 2, same). Out_shape = {}".format(
x.get_shape()))
x = Conv1D(4, kernel_size=2, padding='same')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Conv1D --> Conv1D(64, 2, same). Out_shape = {} \n".format(
x.get_shape()))
in_samp_shape = x.get_shape()
x = UpSampling1D(2)(x)
print("Upsampling pool_size = 2")
print("UpSampling(2nd dec): in_shape= {}\tOut_shape = {}".format(
in_samp_shape, x.get_shape()))
print(" ")
x = Conv1D(2, kernel_size=3, padding='valid')(x)
#Rajouter une couche BN
x = Activation('relu')(x)
print("Upsampled --> Conv1D(32, 2, same). Out_shape = {}".format(
x.get_shape()))
x = Conv1D(1, kernel_size=1, padding='valid')(x)
#Rajouter une couche BN
output = Activation('relu')(x)
print("Conv1D --> Conv1D(1, 1, same) (Output). Out_shape = {}".format(
x.get_shape()))
unet = Model(input_data, output, name='u_net_out')
return unet
def unet_model_3d(input_shape,
n_labels,
batch_normalization=False,
initial_learning_rate=0.00001,
metrics=m.dice_coef):
"""
input_shape:without batch_size,(img_height,img_width,img_depth)
metrics:
"""
inputs = Input(input_shape)
down_layer = []
layer = inputs
# down_layer_1
layer = res_block_v2_3d(layer, 32, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling3D(pool_size=[2, 2, 2],
strides=[2, 2, 2],
padding='same')(layer)
print(str(layer.get_shape()))
# down_layer_2
layer = res_block_v2_3d(layer, 64, batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling3D(pool_size=[2, 2, 2],
strides=[2, 2, 2],
padding='same')(layer)
print(str(layer.get_shape()))
# down_layer_3
layer = res_block_v2_3d(layer,
128,
batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling3D(pool_size=[2, 2, 2],
strides=[2, 2, 2],
padding='same')(layer)
print(str(layer.get_shape()))
# down_layer_4
layer = res_block_v2_3d(layer,
256,
batch_normalization=batch_normalization)
down_layer.append(layer)
layer = MaxPooling3D(pool_size=[2, 2, 2],
strides=[2, 2, 2],
padding='same')(layer)
print(str(layer.get_shape()))
# bottle_layer
layer = res_block_v2_3d(layer,
512,
batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_4
layer = up_and_concate_3d(layer, down_layer[3])
layer = res_block_v2_3d(layer,
256,
batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_3
layer = up_and_concate_3d(layer, down_layer[2])
layer = res_block_v2_3d(layer,
128,
batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_2
layer = up_and_concate_3d(layer, down_layer[1])
layer = res_block_v2_3d(layer, 64, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# up_layer_1
layer = up_and_concate_3d(layer, down_layer[0])
layer = res_block_v2_3d(layer, 32, batch_normalization=batch_normalization)
print(str(layer.get_shape()))
# score_layer
layer = Conv3D(n_labels, [1, 1, 1], strides=[1, 1, 1])(layer)
print(str(layer.get_shape()))
# softmax
layer = Activation('softmax')(layer)
print(str(layer.get_shape()))
outputs = layer
model = Model(inputs=inputs, outputs=outputs)
metrics = [metrics]
model = multi_gpu_model(model, gpus=2)
model.summary()
model.compile(optimizer=Adam(lr=initial_learning_rate),
loss='categorical_crossentropy',
metrics=metrics)
return model
def CombCN(input_frame,
input_video,
video_size=None,
sampling_frame=8,
frame_net_mid_depth=4):
#frame_3DCN => total frames or jsut frame of Vk in Vin
Activ = lambda x: LeakyReLU(alpha=0.2)(x)
Bat = lambda x: BatchNormalization()(x)
video_size = None
if not video_size:
W = 320
H = 240
else:
W = video_size[0]
H = video_size[1]
if input_frame is None:
input_frame = Input(shape=(W, H, 3))
if input_video is None:
input_video = Input(shape=(sampling_frame, W / 2, H / 2, 3))
print(input_frame.get_shape())
e0 = Conv2D(filters=32, padding='same', kernel_size=5)(input_frame)
#e0 = Bat(e0)
e0 = Activ(e0)
print(e0.get_shape())
#WITH NO CONCATENATE Init_dataloader()DING IN 3DCN BUT WITH CONCAT FOR ENCODING IN combination part
e0_C = Conv2D(filters=64, padding='same', kernel_size=3, strides=2)(e0)
e0_C = Bat(e0_C)
e0_C = Activ(e0_C)
print(e0_C.get_shape())
#skip_subnet = CN3D(video_info=None , input_video = input_video, sampling_frame= 8, vid_net_mid_depth = 3)
skip_subnet = input_video
size_subnet = skip_subnet.get_shape()
# IS IT WHAT THE PAPER SAYS? NOT SURE
skip_subnet = Reshape((int(W / 2), int(H / 2),
int(size_subnet[1] * size_subnet[4])))(skip_subnet)
skip_subnet = Conv2D(filters=128,
padding='same',
kernel_size=5,
strides=1,
name='subnet')(skip_subnet)
skip_subnet = Bat(skip_subnet)
skip_subnet = Activ(skip_subnet)
skip_subnet = Conv2D(filters=64,
padding='same',
kernel_size=3,
strides=1,
name='subnet_2')(skip_subnet)
skip_subnet = Bat(skip_subnet)
skip_subnet = Activ(skip_subnet)
e0_C = Concatenate()([e0_C, skip_subnet])
print(e0_C.get_shape())
e1 = Conv2D(filters=256, padding='same', kernel_size=3)(e0_C)
e1 = Bat(e1)
e1 = Activ(e1)
print(e1.get_shape())
e1_C = Conv2D(filters=512, padding='same', kernel_size=4, strides=2)(e1)
e1_C = Bat(e1_C)
e1_C = Activ(e1_C)
print(e1_C.get_shape())
e2 = Conv2D(filters=512, padding='same', kernel_size=3)(e1_C)
e2 = Bat(e2)
e2 = Activ(e2)
print(e2.get_shape())
e2_C = Conv2D(filters=512, padding='same', kernel_size=4, strides=2)(e2)
e2_C = Bat(e2_C)
e2_C = Activ(e2_C)
print(e2_C.get_shape())
fc_mid = e2_C
fc_prev = e2_C
p_num = 2
for i in range(frame_net_mid_depth):
fc_mid = Conv2D(filters=512,
dilation_rate=p_num,
kernel_size=3,
padding='same')(fc_mid)
fc_mid = Bat(fc_mid)
fc_mid = Activ(fc_mid)
f_temp = fc_mid
fc_mid = Concatenate()([fc_mid, fc_prev])
fc_prev = f_temp
print(fc_mid.get_shape())
p_num = p_num * 2
#fc_mid = Deconvolution3D(strides=2, filters=64, kernel_size= 4, padding='same')(fc_mid)
d0 = Concatenate()([fc_prev, e2_C])
d0 = Conv2D(strides=1, filters=512, kernel_size=3, padding='same')(d0)
d0 = Bat(d0)
d0 = Activ(d0)
print(d0.get_shape())
d0_C = UpSampling2D()(d0)
d0_C = Concatenate()([d0_C, e2])
d0_C = Conv2D(strides=1, filters=512, kernel_size=4, padding='same')(d0_C)
d0_C = Bat(d0_C)
d0_C = Activ(d0_C)
print(d0_C.get_shape())
d0_CC = Concatenate()([d0_C, e1_C])
d0_CC = Conv2D(filters=512, padding='same', kernel_size=3)(d0_CC)
d0_CC = Bat(d0_CC)
d0_CC = Activ(d0_CC)
print(d0_CC.get_shape())
d1 = UpSampling2D()(d0_CC)
d1 = Concatenate()([d1, e1])
d1 = Conv2D(strides=1, filters=256, kernel_size=4, padding='same')(d1)
d1 = Bat(d1)
d1 = Activ(d1)
print(d1.get_shape())
d1_C = Concatenate()([d1, e0_C])
d1_C = Conv2D(filters=128, padding='same', kernel_size=3)(d1_C)
d1_C = Bat(d1_C)
d1_C = Activ(d1_C)
print(d1_C.get_shape())
d1_CC = UpSampling2D()(d1_C)
d1_CC = Conv2D(strides=1, filters=64, kernel_size=4, padding='same')(d1_CC)
d1_CC = Bat(d1_CC)
d1_CC = Activ(d1_CC)
print(d1_CC.get_shape())
d1_CCC = Concatenate()([d1_CC, e0])
d1_CCC = Conv2D(strides=1, filters=48, kernel_size=4,
padding='same')(d1_CCC)
d1_CCC = Bat(d1_CCC)
d1_CCC = Activ(d1_CCC)
print(d1_CCC.get_shape())
d_out = Conv2D(filters=3, padding='same', kernel_size=3)(d1_CCC)
#d_out = Bat(d_out)
d_out = Activation('tanh')(d_out)
print(d_out.get_shape())
return d_out
def get_Model_VGG(training):
if training:
keeppro = 0.2
else:
keeppro = 0
input_shape = (Global.IMG_WIDTH, Global.IMG_HEIGHT, Global.IMG_CHANNELS)
# Make Networkw
inputs = Input(name='the_input', shape=input_shape, dtype='float32')
inner = Conv2D(64, (3, 3), padding='same', name='conv1')(inputs)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 2), name='max1')(inner)
inner = Conv2D(128, (3, 3), padding='same', name='conv2')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 2), name='max2')(inner)
inner = Conv2D(256, (3, 3), padding='same', name='conv3')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = Conv2D(256, (3, 3),
padding='same',
name='conv4',
kernel_initializer='he_normal')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 2),
name='max3')(inner) # (None, 32, 8, 256)
inner = Conv2D(512, (3, 3),
padding='same',
name='conv5',
kernel_initializer='he_normal')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = Conv2D(512, (3, 3), padding='same', name='conv6')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(1, 2), name='max4')(inner)
inner = Conv2D(512, (2, 2),
padding='same',
kernel_initializer='he_normal',
name='con7')(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
# CNN to RNN
conv_shape = inner.get_shape()
inner = Reshape(target_shape=(int(conv_shape[1]),
int(conv_shape[2] * conv_shape[3])))(inner)
inner = Dense(64,
activation='relu',
kernel_initializer='he_normal',
name='dense1')(inner)
inner = Dropout(keeppro)(inner)
# RNN layer
gru_1 = GRU(256,
return_sequences=True,
kernel_initializer='he_normal',
name='gru1',
dropout=keeppro)(inner)
gru_1b = GRU(256,
return_sequences=True,
go_backwards=True,
kernel_initializer='he_normal',
name='gru1_b',
dropout=keeppro)(inner)
gru1_merged = add([gru_1, gru_1b])
gru1_merged = BatchNormalization()(gru1_merged)
gru_2 = GRU(256,
return_sequences=True,
kernel_initializer='he_normal',
name='gru2',
dropout=keeppro)(gru1_merged)
gru_2b = GRU(256,
return_sequences=True,
go_backwards=True,
kernel_initializer='he_normal',
name='gru2_b',
dropout=keeppro)(gru1_merged)
gru2_merged = concatenate([gru_2, gru_2b])
gru2_merged = BatchNormalization()(gru2_merged)
# transforms RNN output to character activations:
inner = Dense(len(Global.CHAR_SET) + 1,
kernel_initializer='he_normal',
name='dense2')(gru2_merged)
# 防止过拟合
#inner = Dropout(keeppro)(inner)
y_pred = Activation('softmax', name='softmax')(inner)
labels = Input(name='the_labels', shape=[Global.WORD_LEN], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
loss_out = Lambda(ctc_lambda_func, output_shape=(1, ),
name='ctc')([y_pred, labels, input_length,
label_length]) # (None, 1)
if training:
return Model(inputs=[inputs, labels, input_length, label_length],
outputs=[loss_out])
else:
return Model(inputs=[inputs], outputs=y_pred)
def get_Model_2(training):
if training:
keeppro = 0.2
else:
keeppro = 0.
input_shape = (Global.IMG_WIDTH, Global.IMG_HEIGHT, Global.IMG_CHANNELS)
inputs = Input(name='the_input', shape=input_shape, dtype='float32')
inner = Conv2D(32, (3, 3),
padding='same',
name='conv1',
kernel_initializer='he_normal',
strides=1)(inputs)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 2),
name='max1',
padding='same',
strides=2)(inner)
inner = Conv2D(64, (3, 3),
padding='same',
name='conv3',
kernel_initializer='he_normal',
strides=1)(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 1),
name='max2',
padding='same',
strides=2)(inner)
inner = Conv2D(128, (3, 3),
padding='same',
name='conv5',
kernel_initializer='he_normal',
strides=1)(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 1),
name='max3',
padding='same',
strides=2)(inner)
inner = Conv2D(256, (3, 3),
padding='same',
name='conv6',
kernel_initializer='he_normal',
strides=1)(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
inner = MaxPooling2D(pool_size=(2, 1),
name='max4',
padding='same',
strides=2)(inner)
inner = Conv2D(256, (3, 3),
padding='same',
name='conv7',
kernel_initializer='he_normal',
strides=1)(inner)
inner = Activation('relu')(inner)
inner = BatchNormalization()(inner)
conv_shape = inner.get_shape()
inner = TimeDistributed(Flatten(), name='timedistrib')(inner)
inner = Dropout(keeppro)(inner)
gru_1 = GRU(128,
return_sequences=True,
kernel_initializer='he_normal',
name='gru1',
dropout=keeppro)(inner)
gru_1b = GRU(128,
return_sequences=True,
go_backwards=True,
kernel_initializer='he_normal',
name='gru1_b',
dropout=keeppro)(inner)
gru1_merged = add([gru_1, gru_1b])
gru1_merged = BatchNormalization()(gru1_merged)
gru_2 = GRU(128,
return_sequences=True,
kernel_initializer='he_normal',
name='gru2',
dropout=keeppro)(gru1_merged)
gru_2b = GRU(128,
return_sequences=True,
go_backwards=True,
kernel_initializer='he_normal',
name='gru2_b',
dropout=keeppro)(gru1_merged)
gru2_merged = concatenate([gru_2, gru_2b])
gru2_merged = BatchNormalization()(gru2_merged)
inner = Dense(len(Global.CHAR_SET) + 1,
kernel_initializer='he_normal',
name='dense2')(gru2_merged)
# 防止过拟合
y_pred = Activation('softmax', name='softmax')(inner)
labels = Input(name='the_labels', shape=[Global.WORD_LEN], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
loss_out = Lambda(ctc_lambda_func, output_shape=(1, ),
name='ctc')([y_pred, labels, input_length, label_length])
if training:
return Model(inputs=[inputs, labels, input_length, label_length],
outputs=[loss_out])
else:
return Model(inputs=[inputs], outputs=y_pred)