标签:单机 Keras 多卡 checkpoint train test import model save
注意:此模式下不能用fit_generator() 方式训练
""" GPU test """ import os import sys os.system('pip install -i https://pypi.tuna.tsinghua.edu.cn/simple keras==2.3.1') from tensorflow.keras import Sequential from tensorflow.keras.models import Model from tensorflow.keras.layers import Input,Dense from tensorflow.keras import layers from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping import tensorflow as tf from tensorflow import keras import numpy as np import pickle import time checkpoint_save_dir = "/models/embedding_recall/ckpt" best_model_name = "best_model.hdf5" if not os.path.exists(checkpoint_save_dir): os.makedirs(checkpoint_save_dir) with open(r"/models/embedding_recall/resources/minist.pkl","rb") as fr: data = pickle.load(fr) def make_or_restore_model(): # Either restore the latest model, or create a fresh one # if there is no checkpoint available. checkpoints = [checkpoint_dir + "/" + name for name in os.listdir(checkpoint_dir)] if checkpoints: latest_checkpoint = max(checkpoints, key=os.path.getctime) print("Restoring from", latest_checkpoint) return keras.models.load_model(latest_checkpoint) print("Creating a new model") return get_compiled_model() def get_compiled_model(): inputs = Input(shape=(784,)) inputs.shape inputs.dtype dense = Dense(64, activation="relu") x = dense(inputs) x = Dense(64, activation="relu")(x) outputs = Dense(10)(x) model = Model(inputs=inputs, outputs=outputs, name="my_model") model.compile( loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer = keras.optimizers.RMSprop(), metrics = ["accuracy"],) return model def make_or_restore_model(checkpoint_save_dir, model_name): # Either restore the latest model, or create a fresh one # if there is no checkpoint available. if checkpoint_save_dir: latest_checkpoint = os.path.join(checkpoint_save_dir, model_name) print("Restoring from", latest_checkpoint) return keras.models.load_model(latest_checkpoint) else: return None def run_training(epochs): strategy = tf.distribute.MirroredStrategy() print("Number of devices:{}".format(strategy.num_replicas_in_sync)) with strategy.scope(): model = get_compiled_model() (x_train, y_train),(x_test, y_test) = data[0],data[1] x_train = x_train.reshape(60000, 784).astype("float32")/255 x_test = x_test.reshape(10000, 784).astype("float32")/255 early_stop = EarlyStopping(monitor='loss', patience=3, verbose=1) checkpoint = ModelCheckpoint(os.path.join(checkpoint_save_dir, best_model_name), monitor='loss', verbose=1, save_best_only=True, mode='min') callbacks_list = [checkpoint, early_stop] t1 = time.time() history = model.fit(x_train, y_train, batch_size=100, epochs=epochs, callbacks=callbacks_list) t2 = time.time() # test_scores = model.evaluate(x_test, y_test, batch_size=100,verbose=2) # print("test loss:{}".format(test_scores[0])) # print("test acc:{}".format(test_scores[1])) # print("total spent:{}".format(t2-t1)) def continue_training(epochs): strategy = tf.distribute.MirroredStrategy() print("Number of devices:{}".format(strategy.num_replicas_in_sync)) # with strategy.scope(): model = make_or_restore_model(checkpoint_save_dir, best_model_name) (x_train, y_train),(x_test, y_test) = data[0],data[1] x_train = x_train.reshape(60000, 784).astype("float32")/255 x_test = x_test.reshape(10000, 784).astype("float32")/255 early_stop = EarlyStopping(monitor='loss', patience=3, verbose=1) checkpoint = ModelCheckpoint(os.path.join(checkpoint_save_dir, best_model_name), monitor='loss', verbose=1, save_best_only=True, mode='min') callbacks_list = [checkpoint, early_stop] t1 = time.time() history = model.fit(x_train, y_train, batch_size=100, epochs=epochs, callbacks=callbacks_list) t2 = time.time() run_training(epochs=5) # continue_training(epochs=100)
标签:单机,Keras,多卡,checkpoint,train,test,import,model,save 来源: https://www.cnblogs.com/demo-deng/p/15856442.html
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