DeepHiC/training.py at master · biocai/DeepHiC · GitHub

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import keras
from keras import regularizers
from keras.optimizers import Adagrad,Adam,SGD
from keras.callbacks import TensorBoard
from keras import Sequential,backend
from keras.layers import Dense,Conv2D,Conv1D,MaxPooling2D,Dropout,Dense,Flatten,BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping
from keras import initializers
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True  # TensorFlow按需分配显存
config.gpu_options.per_process_gpu_memory_fraction = 1 # 指定显存分配比例
keras.backend.tensorflow_backend.set_session(tf.Session(config=config))

train_datagen = ImageDataGenerator(
    horizontal_flip=True,vertical_flip=True)
val_datagen = ImageDataGenerator()

train_generator = train_datagen.flow_from_directory(directory='./train/',
                                  target_size=(40000,16),
                                  batch_size=32,color_mode="grayscale")

val_generator = val_datagen.flow_from_directory(directory='./test/',
                                  target_size=(40000,16),
                                batch_size=32,color_mode="grayscale")


def define_model():
    model =Sequential()
    model.add(Conv2D(64,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu',input_shape=(40000,16,1)))
    model.add(Conv2D(64,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu'))
    model.add(Conv2D(64,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu'))
    model.add(MaxPooling2D(pool_size=(4,1),strides=(2, 1)))
    model.add(BatchNormalization())
    model.add(Conv2D(128,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu'))
    model.add(Conv2D(128,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu'))
    model.add(Conv2D(128,kernel_size=[24,1],strides=[4,1],padding="same",activation='relu'))
    model.add(MaxPooling2D(pool_size=(4,1),strides=(2, 1)))
    model.add(Flatten())
    model.add(BatchNormalization())
    model.add(Dense(2048, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2048, activation='relu'))
    model.add(Dense(2,activation='softmax'))
    return model


model =define_model()
model.summary()
early_stopping = EarlyStopping(monitor='val_loss', patience=50,restore_best_weights=True)
def setup_to_train(model):
    model.compile(optimizer=Adam(lr=0.0001,decay=0.00001),loss='categorical_crossentropy',metrics=['accuracy'])

setup_to_train(model)
history=model.fit_generator(generator=train_generator,
                    epochs=50,
                    validation_data=val_generator,
                    validation_steps=2000,
                    class_weight='auto',callbacks=[early_stopping])
model.save('deephic_model_hesc.h5')