我是 Keras、Tensorflow、Python 的新手,我正在尝试构建一个供个人使用/未来学习的模型。我刚刚开始使用 python,并想出了这段代码(在视频和教程的帮助下)。我的问题是,我的 Python 内存使用量在每个时期甚至在构建新模型之后都在慢慢增加。一旦内存达到 100%,训练就会停止,不会出现错误/警告。我知道的不多,但问题应该出在循环内的某个地方(如果我没记错的话)。我知道关于
k.clear.session()
但问题要么没有消除,要么我不知道如何将其集成到我的代码中。
我有:
Python 版本 3.6.4,
Tensorflow 2.0.0rc1(CPU版本),
喀拉斯2.3.0
这是我的代码:
import pandas as pd
import os
import time
import tensorflow as tf
import numpy as np
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint
EPOCHS = 25
BATCH_SIZE = 32
df = pd.read_csv("EntryData.csv", names=['1SH5', '1SHA', '1SA5', '1SAA', '1WH5', '1WHA',
'2SA5', '2SAA', '2SH5', '2SHA', '2WA5', '2WAA',
'3R1', '3R2', '3R3', '3R4', '3R5', '3R6',
'Target'])
df_val = 14554
validation_df = df[df.index > df_val]
df = df[df.index <= df_val]
train_x = df.drop(columns=['Target'])
train_y = df[['Target']]
validation_x = validation_df.drop(columns=['Target'])
validation_y = validation_df[['Target']]
train_x = np.asarray(train_x)
train_y = np.asarray(train_y)
validation_x = np.asarray(validation_x)
validation_y = np.asarray(validation_y)
train_x = train_x.reshape(train_x.shape[0], 1, train_x.shape[1])
validation_x = validation_x.reshape(validation_x.shape[0], 1, validation_x.shape[1])
dense_layers = [0, 1, 2]
layer_sizes = [32, 64, 128]
conv_layers = [1, 2, 3]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size,
dense_layer, int(time.time()))
tensorboard = TensorBoard(log_dir="logs\{}".format(NAME))
print(NAME)
model = Sequential()
model.add(LSTM(layer_size, input_shape=(train_x.shape[1:]),
return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())
for l in range(conv_layer-1):
model.add(LSTM(layer_size, return_sequences=True))
model.add(Dropout(0.1))
model.add(BatchNormalization())
for l in range(dense_layer):
model.add(Dense(layer_size, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
# Compile model
model.compile(loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
# unique file name that will include the epoch
# and the validation acc for that epoch
filepath = "RNN_Final.{epoch:02d}-{val_accuracy:.3f}"
checkpoint = ModelCheckpoint("models\{}.model".format(filepath,
monitor='val_acc', verbose=0, save_best_only=True,
mode='max')) # saves only the best ones
# Train model
history = model.fit(
train_x, train_y,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(validation_x, validation_y),
callbacks=[tensorboard, checkpoint])
# Score model
score = model.evaluate(validation_x, validation_y, verbose=2)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# Save model
model.save("models\{}".format(NAME))
另外我不知道是否可以在 1 个问题中提出 2 个问题(我不想在这里用我的问题来垃圾邮件,任何有 python 经验的人都可以在一分钟内解决),但我也有问题检查点保存。我只想保存性能最佳的模型(每 1 个 NN 规范 1 个模型 - 节点/层数),但目前它是在每个 epoch 后保存的。如果这样问不合适,我可以为此创建另一个问题。
非常感谢您的帮助。