def ModelShare():
tweet_a = Input(shape=(280, 256))
tweet_b = Input(shape=(280, 256))
# This layer can take as input a matrix
# and will return a vector of size 64
shared_lstm = LSTM(64, return_sequences=True, name='lstm')
# When we reuse the same layer instance
# multiple times, the weights of the layer
# are also being reused
# (it is effectively *the same* layer)
encoded_a = shared_lstm(tweet_a)
encoded_b = shared_lstm(tweet_b)
# We can then concatenate the two vectors:
merged_vector = concatenate([encoded_a, encoded_b], axis=-1)
# And add a logistic regression on top
predictions = Dense(1, activation='sigmoid')(merged_vector)
# We define a trainable model linking the
# tweet inputs to the predictions
model = Model(inputs=[tweet_a, tweet_b], outputs=predictions)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def modelDemoStandardConvLSTMInception(input_shape, parameter=None):
# define LSTM
input = Input(shape=input_shape, name='main_input')
I_1 = TimeDistributed(Conv2D(16, (1, 1),
activation='relu',
padding='same',
name='C_1'),
name='I_11')(input)
I_1 = TimeDistributed(Conv2D(16, (5, 5),
activation='relu',
padding='same',
name='C_2'),
name='I_12')(I_1)
I_2 = TimeDistributed(MaxPooling2D((3, 3),
strides=(1, 1),
padding='same',
name='C_3'),
name='I_21')(input)
I_2 = TimeDistributed(Conv2D(16, (1, 1),
activation='relu',
padding='same',
name='C_4'),
name='I_22')(I_2)
concatenate_output = concatenate([I_1, I_2], axis=-1)
# x = TimeDistributed(Flatten())(x)
x = ConvLSTM2D(filters=32,
kernel_size=(3, 3),
padding='same',
return_sequences=False)(concatenate_output)
#x = MaxPooling2D((3, 3), strides=(1, 1), padding='same', name='M_1')(x)
x = (Flatten())(x)
x = RepeatVector(8)(x)
x = LSTM(50, return_sequences=True)(x)
output = TimeDistributed(Dense(8, activation='softmax'),
name='main_output')(x)
#with tensorflow.device('/cpu'):
model = Model(inputs=[input], outputs=[output])
# compile the model with gpu
#parallel_model = multi_gpu_model(model, gpus=2)
#parallel_model.compile(loss={'main_output': 'categorical_crossentropy'},
# loss_weights={'main_output': 1.}, optimizer='adam', metrics=['accuracy'])
#model = multi_gpu(model, gpus=[1, 2])
model.compile(loss={'main_output': 'categorical_crossentropy'},
loss_weights={'main_output': 1.},
optimizer='adam',
metrics=['accuracy'])
return model
def modelB(row, col, parameter=None):
# define LSTM
input = Input(shape=(None, row, col, 1), name='main_input')
''' x = TimeDistributed(Conv2D(16, (2, 2)))(input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(0.25)(x)
'''
# tower_1 = TimeDistributed(Conv2D(16, (1, 1), padding='same', activation='relu'))(input)
# tower_1 = TimeDistributed(Conv2D(16, (3, 3), padding='same', activation='relu'))(tower_1)
tower_2 = TimeDistributed(Conv2D(16, (1, 1), padding='same'))(input)
x = BatchNormalization()(tower_2)
x = Activation('relu')(x)
x = Dropout(0.25)(x)
tower_2 = TimeDistributed(Conv2D(16, (5, 5), padding='same'))(x)
x = BatchNormalization()(tower_2)
x = Activation('relu')(x)
tower_2 = Dropout(0.25)(x)
tower_3 = TimeDistributed(
MaxPooling2D((3, 3), strides=(1, 1), padding='same'))(input)
tower_3 = TimeDistributed(Conv2D(16, (1, 1), padding='same'))(tower_3)
x = BatchNormalization()(tower_3)
x = Activation('relu')(x)
tower_3 = Dropout(0.25)(x)
concatenate_output = concatenate([tower_2, tower_3], axis=-1)
x = TimeDistributed(MaxPooling2D(pool_size=(2, 2),
strides=2))(concatenate_output)
x = Dropout(0.25)(x)
x = TimeDistributed(Flatten())(x)
# convLstm = ConvLSTM2D(filters=40, kernel_size=(3, 3),padding='same', return_sequences=False)(x)
lstm_output = LSTM(75)(x)
lstm_output = BatchNormalization()(lstm_output)
# lstm_output = BatchNormalization()(convLstm)
# auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_output)
# auxiliary_input = Input(shape=(4,), name='aux_input')
# x = concatenate([lstm_output, auxiliary_input])
x = RepeatVector(4)(lstm_output)
x = LSTM(50, return_sequences=True)(x)
# model.add(Dropout(0.25))
x = BatchNormalization()(x)
output = TimeDistributed(Dense(4, activation='softmax'),
name='main_output')(x)
model = Model(inputs=[input], outputs=[output])
model.compile(loss={'main_output': 'categorical_crossentropy'},
loss_weights={'main_output': 1.},
optimizer='adam',
metrics=['accuracy'])
return model
def ModelVisualQuestionAnswering():
# First, let's define a vision model using a Sequential model.
# This model will encode an image into a vector.
vision_model = Sequential()
vision_model.add(
Conv2D(64, (3, 3),
activation='relu',
padding='same',
input_shape=(224, 224, 3)))
vision_model.add(Conv2D(64, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(128, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Flatten())
# Now let's get a tensor with the output of our vision model:
image_input = Input(shape=(224, 224, 3))
encoded_image = vision_model(image_input)
# Next, let's define a language model to encode the question into a vector.
# Each question will be at most 100 words long,
# and we will index words as integers from 1 to 9999.
question_input = Input(shape=(100, ), dtype='int32')
embedded_question = Embedding(input_dim=10000,
output_dim=256,
input_length=100)(question_input)
encoded_question = LSTM(256)(embedded_question)
# Let's concatenate the question vector and the image vector:
merged = concatenate([encoded_question, encoded_image])
# And let's train a logistic regression over 1000 words on top:
output = Dense(1000, activation='softmax')(merged)
# This is our final model:
vqa_model = Model(inputs=[image_input, question_input], outputs=output)
return vqa_model
def ModelInception():
input_img = Input(shape=(256, 256, 3))
tower_1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_1 = Conv2D(64, (3, 3), padding='same', activation='relu')(tower_1)
tower_2 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_2 = Conv2D(64, (5, 5), padding='same', activation='relu')(tower_2)
tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img)
tower_3 = Conv2D(64, (1, 1), padding='same', activation='relu')(tower_3)
output = concatenate([tower_1, tower_2, tower_3], axis=1)
model = Model(inputs=[input_img], outputs=output)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def ModelSharedVision():
# First, define the vision modules
digit_input = Input(shape=(27, 27, 1))
x = Conv2D(64, (3, 3))(digit_input)
x = Conv2D(64, (3, 3))(x)
x = MaxPooling2D((2, 2))(x)
out = Flatten()(x)
vision_model = Model(digit_input, out)
# Then define the tell-digits-apart model
digit_a = Input(shape=(27, 27, 1))
digit_b = Input(shape=(27, 27, 1))
# The vision model will be shared, weights and all
out_a = vision_model(digit_a)
out_b = vision_model(digit_b)
concatenated = concatenate([out_a, out_b])
out = Dense(1, activation='sigmoid')(concatenated)
classification_model = Model([digit_a, digit_b], out)
return classification_model
def modelA(row, col):
# define LSTM
input = Input(shape=(None, row, col, 1), name='main_input')
x = TimeDistributed(Conv2D(16, (2, 2), activation='relu'))(input)
x = Dropout(0.25)(x)
x = BatchNormalization()(x)
x = TimeDistributed(MaxPooling2D(pool_size=(2, 2), strides=2))(x)
x = Dropout(0.25)(x)
x = TimeDistributed(Flatten())(x)
lstm_output = LSTM(75)(x)
lstm_output = BatchNormalization()(lstm_output)
auxiliary_output = Dense(1, activation='sigmoid',
name='aux_output')(lstm_output)
auxiliary_input = Input(shape=(4, ), name='aux_input')
x = concatenate([lstm_output, auxiliary_input])
x = RepeatVector(8)(x)
x = LSTM(50, return_sequences=True)(x)
# model.add(Dropout(0.25))
x = BatchNormalization()(x)
output = TimeDistributed(Dense(5, activation='softmax'),
name='main_output')(x)
model = Model(inputs=[input, auxiliary_input],
outputs=[output, auxiliary_output])
model.compile(loss={
'main_output': 'categorical_crossentropy',
'aux_output': 'binary_crossentropy'
},
loss_weights={
'main_output': 1.,
'aux_output': 0.2
},
optimizer='adam',
metrics=['accuracy'])
return model
def ModelVideoQuestionAnswering():
# First, let's define a vision model using a Sequential model.
# This model will encode an image into a vector.
vision_model = Sequential()
vision_model.add(
Conv2D(64, (3, 3),
activation='relu',
padding='same',
input_shape=(224, 224, 3)))
vision_model.add(Conv2D(64, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(128, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Flatten())
# Now let's get a tensor with the output of our vision model:
image_input = Input(shape=(224, 224, 3))
encoded_image = vision_model(image_input)
# Next, let's define a language model to encode the question into a vector.
# Each question will be at most 100 words long,
# and we will index words as integers from 1 to 9999.
question_input = Input(shape=(100, ), dtype='int32')
embedded_question = Embedding(input_dim=10000,
output_dim=256,
input_length=100)(question_input)
encoded_question = LSTM(256)(embedded_question)
# Let's concatenate the question vector and the image vector:
merged = concatenate([encoded_question, encoded_image])
# And let's train a logistic regression over 1000 words on top:
output = Dense(1000, activation='softmax')(merged)
# This is our final model:
# vqa_model = Model(inputs=[image_input, question_input], outputs=output)
video_input = Input(shape=(100, 224, 224, 3))
# This is our video encoded via the previously trained vision_model (weights are reused)
encoded_frame_sequence = TimeDistributed(vision_model)(
video_input) # the output will be a sequence of vectors
encoded_video = LSTM(256)(
encoded_frame_sequence) # the output will be a vector
# This is a model-level representation of the question encoder, reusing the same weights as before:
question_encoder = Model(inputs=question_input, outputs=encoded_question)
# Let's use it to encode the question:
video_question_input = Input(shape=(100, ), dtype='int32')
encoded_video_question = question_encoder(video_question_input)
# And this is our video question answering model:
merged = concatenate([encoded_video, encoded_video_question])
output = Dense(1000, activation='softmax')(merged)
video_qa_model = Model(inputs=[video_input, video_question_input],
outputs=output)
return video_qa_model
def construct_keras_api_model(embedding_weights):
# input_no_time_no_repeat = Input(shape=max_len, dtype='int32')
# embedded_no_time_no_repeat = Embedding(
# creative_id_window,embedding_size,weights=[embedding_weights],trainable=False
# )(input_no_time_no_repeat)
# ==================================================================================
Input_fix_creative_id = Input(shape=(math.ceil(time_id_max / period_days) *
period_length),
dtype='int32',
name='input_fix_creative_id')
Embedded_fix_creative_id = Embedding(
creative_id_window,
embedding_size,
weights=[embedding_weights],
trainable=False)(Input_fix_creative_id)
# ==================================================================================
# input_no_time_with_repeat = Input(shape=max_len, dtype='int32')
# embedded_no_time_with_repeat = Embedding(creative_id_window,embedding_size,weights=[embedding_weights],trainable=False)(input_no_time_with_repeat)
# ----------------------------------------------------------------------
GM_x = keras.layers.GlobalMaxPooling1D()(Embedded_fix_creative_id)
GM_x = Dropout(0.5)(GM_x)
GM_x = Dense(embedding_size // 2, kernel_regularizer=l2(0.001))(GM_x)
GM_x = BatchNormalization()(GM_x)
GM_x = Activation('relu')(GM_x)
GM_x = Dropout(0.5)(GM_x)
GM_x = Dense(embedding_size // 4, kernel_regularizer=l2(0.001))(GM_x)
GM_x = BatchNormalization()(GM_x)
GM_x = Activation('relu')(GM_x)
GM_x = Dense(1, 'sigmoid')(GM_x)
# ----------------------------------------------------------------------
GA_x = GlobalAveragePooling1D()(Embedded_fix_creative_id)
GA_x = Dropout(0.5)(GA_x)
GA_x = Dense(embedding_size // 2, kernel_regularizer=l2(0.001))(GA_x)
GA_x = BatchNormalization()(GA_x)
GA_x = Activation('relu')(GA_x)
GA_x = Dropout(0.5)(GA_x)
GA_x = Dense(embedding_size // 4, kernel_regularizer=l2(0.001))(GA_x)
GA_x = BatchNormalization()(GA_x)
GA_x = Activation('relu')(GA_x)
GA_x = Dense(1, 'sigmoid')(GA_x)
# ==================================================================================
Conv_creative_id = Conv1D(embedding_size, 15, 5,
activation='relu')(Embedded_fix_creative_id)
# ----------------------------------------------------------------------
Conv_GM_x = MaxPooling1D(7)(Conv_creative_id)
Conv_GM_x = Conv1D(embedding_size, 2, 1, activation='relu')(Conv_GM_x)
Conv_GM_x = GlobalMaxPooling1D()(Conv_GM_x)
Conv_GM_x = Dropout(0.5)(Conv_GM_x)
Conv_GM_x = Dense(embedding_size // 2,
kernel_regularizer=l2(0.001))(Conv_GM_x)
Conv_GM_x = BatchNormalization()(Conv_GM_x)
Conv_GM_x = Activation('relu')(Conv_GM_x)
Conv_GM_x = Dropout(0.5)(Conv_GM_x)
Conv_GM_x = Dense(embedding_size // 4,
kernel_regularizer=l2(0.001))(Conv_GM_x)
Conv_GM_x = BatchNormalization()(Conv_GM_x)
Conv_GM_x = Activation('relu')(Conv_GM_x)
Conv_GM_x = Dense(1, 'sigmoid')(Conv_GM_x)
# ----------------------------------------------------------------------
Conv_GA_x = AveragePooling1D(7)(Conv_creative_id)
Conv_GA_x = Conv1D(embedding_size, 2, 1, activation='relu')(Conv_GA_x)
Conv_GA_x = GlobalAveragePooling1D()(Conv_GA_x)
Conv_GA_x = Dropout(0.5)(Conv_GA_x)
Conv_GA_x = Dense(embedding_size // 2,
kernel_regularizer=l2(0.001))(Conv_GA_x)
Conv_GA_x = BatchNormalization()(Conv_GA_x)
Conv_GA_x = Activation('relu')(Conv_GA_x)
Conv_GA_x = Dropout(0.5)(Conv_GA_x)
Conv_GA_x = Dense(embedding_size // 4,
kernel_regularizer=l2(0.001))(Conv_GA_x)
Conv_GA_x = BatchNormalization()(Conv_GA_x)
Conv_GA_x = Activation('relu')(Conv_GA_x)
Conv_GA_x = Dense(1, 'sigmoid')(Conv_GA_x)
# ----------------------------------------------------------------------
LSTM_x = Conv1D(embedding_size, 14, 7, activation='relu')(Conv_creative_id)
LSTM_x = LSTM(embedding_size, return_sequences=True)(LSTM_x)
LSTM_x = LSTM(embedding_size, return_sequences=True)(LSTM_x)
LSTM_x = LSTM(embedding_size)(LSTM_x)
LSTM_x = Dropout(0.5)(LSTM_x)
LSTM_x = Dense(embedding_size // 2, kernel_regularizer=l2(0.001))(LSTM_x)
LSTM_x = BatchNormalization()(LSTM_x)
LSTM_x = Activation('relu')(LSTM_x)
LSTM_x = Dropout(0.5)(LSTM_x)
LSTM_x = Dense(embedding_size // 4, kernel_regularizer=l2(0.001))(LSTM_x)
LSTM_x = BatchNormalization()(LSTM_x)
LSTM_x = Activation('relu')(LSTM_x)
LSTM_x = Dense(1, 'sigmoid')(LSTM_x)
# ----------------------------------------------------------------------
concatenated = concatenate([
GM_x,
GA_x,
Conv_GM_x,
Conv_GA_x,
LSTM_x,
],
axis=-1)
output_tensor = Dense(1, 'sigmoid')(concatenated)
keras_api_model = Model(
[
# input_no_time_no_repeat,
Input_fix_creative_id,
# input_no_time_with_repeat,
],
output_tensor)
keras_api_model.summary()
plot_model(keras_api_model, to_file='model/keras_api_word2vec_model.png')
print('-' * 5 + ' ' * 3 + "编译模型" + ' ' * 3 + '-' * 5)
keras_api_model.compile(optimizer=optimizers.RMSprop(lr=RMSProp_lr),
loss=losses.binary_crossentropy,
metrics=[metrics.binary_accuracy])
return keras_api_model
def call(self,query_input,source_input,bias,training,cache=None,decode_loop_step=None):
"""Apply attention mechanism to query_input and source_input.
Args:
query_input: [B , len_query , hidden_size]
source_input: [B , len_souce , hidden_size ]
bias: [B, 1, len_query, len_source]
training: bool
cache: (Used during prediction) A dictionary with tensors containing
results of previous attentions. The dictionary must have the items:
{'k': tensor with shape [B,i,heads,dim_per_head],
'v': tensor with shape [B,i,heads,dim_per_head]}
where i is the current decoded length for non-padded decode, or max
sequence length for padded decode.
decode_loop_step: An integer, step number of the decoding loop. Used only
for autoregressive inference on TPU.
Returns:
Attention layer output with shape [B,len_query,hidden_size]
"""
# Linearly project query, key and value using different learned
# projections. Splitting heads is automatically done during the linear
# projections --> [B, len, num_heads, dim_per_head]
query=self.query_dense_layer(query_input)
key=self.key_dense_layer(source_input)
value=self.value_dense_layer(source_input)
if cache is not None:
# Combine cached keys and values with new keys and values.
if decode_loop_step is not None:
cache_k_shape=cache['k'].shape.as_list()
indices=tf.reshape(
tf.one_hot(decode_loop_step,cache_k_shape[1],dtype=key.dtype),
[1,cache_k_shape[1],1,1]
)
key=cache['k']+key*indices
cache_v_shape=cache['v'].shape.as_list()
indices=tf.reshape(
tf.one_hot(decode_loop_step,cache_v_shape[1],dtype=value.dtype),
[1,cache_v_shape[1],1,1]
)
value=cache['v']+value*indices
else:
key=layers.concatenate([tf.cast(cache['k'],key.dtype),key],axis=1)
value=layers.concatenate([tf.cast(cache['v'],value.dtype),key],axis=1)
# Update cache
cache['k']=key
cache['v']=value
# Scale query to prevent the dot product between query and key from growing too large.
depth=(self.hidden_size//self.num_heads)
query*=depth**-0.5
# Calculate dot product attention
logits=tf.einsum('BTNH,BFNH->BNFT',key,query)
logits+=bias
# Note that softmax internally performs math operations using float32
# for numeric stability. When training with float16, we keep the input
# and output in float16 for better performance.
weights=layers.Softmax('attention_weights')(logits)
if training:
weights=layers.Dropout(self.attention_dropout)(weights)
attention_output=tf.einsum('BNFT,BTNH->BFNH',weights,value)
# Run the outputs through another linear projection layer. Recombining heads
# is automatically done --> [batch_size, length, hidden_size]
attention_output=self.output_dense_layer(attention_output)
return attention_output
def init_model(self, mode="resnet50"):
"""
初始化模型
:param mode: 模型类型
:return: 模型名称和基础模型
"""
if mode == "resnet50v2":
from tensorflow.keras.applications.resnet_v2 import ResNet50V2
model_name = 'rotnet_v3_resnet50v2_{epoch:02d}_{val_acc:.4f}.hdf5'
base_model = ResNet50V2(weights='imagenet',
include_top=False,
input_shape=self.input_shape)
elif mode == "resnet50":
from tensorflow.keras.applications.resnet import ResNet50
model_name = 'rotnet_v3_resnet50_{epoch:02d}_{val_acc:.4f}.hdf5'
base_model = ResNet50(weights='imagenet',
include_top=False,
input_shape=self.input_shape)
elif mode == "mobilenetv2":
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
model_name = 'rotnet_v3_mobilenetv2_{epoch:02d}_{val_acc:.4f}.hdf5'
base_model = MobileNetV2(weights='imagenet',
include_top=False,
input_shape=self.input_shape)
elif mode == "densenet121":
from tensorflow.python.keras.applications.densenet import DenseNet121
model_name = 'rotnet_v3_densenet121_{epoch:02d}_{val_acc:.4f}.hdf5'
base_model = DenseNet121(weights='imagenet',
include_top=False,
input_shape=self.input_shape)
else:
raise Exception("[Exception] mode {} 不支持!!".format(mode))
# freeze
for layer in base_model.layers:
layer.trainable = True
x = base_model.output
# if mode == "mobilenetv2":
# x = Dense(128, activation="relu")(x)
x = Flatten()(x)
if self.is_hw_ratio: # 是否使用宽高比
x1 = base_model.output
x1 = Flatten()(x1)
input_ratio = Input(shape=(1, ), name='ratio')
x2 = Dense(1, activation='relu')(input_ratio)
x = concatenate([x1, x2])
final_output = Dense(self.nb_classes,
activation='softmax',
name='fc360')(x)
model = Model(inputs=base_model.input, outputs=final_output)
# model.summary()
# 优化器
if self.nb_classes == 360:
metrics = ["acc", angle_error]
else:
metrics = ["acc"]
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=0.0004, momentum=0.9),
metrics=metrics)
if self.model_path:
model.load_weights(self.model_path)
print('[Info] 加载模型的路径: {}'.format(self.model_path))
return model_name, model