因此,我正在使用ModelCheckpoint回调保存我正在训练的模型的最佳时期。它可以保存,没有错误,但是当我尝试加载它时,出现错误:
2019-07-27 22:58:04.713951: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open C:\Users\Riley\PycharmProjects\myNN\cp.ckpt: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?
我尝试使用绝对/完整路径,但是没有运气。我确定我可以使用EarlyStopping,但我仍然想了解为什么会收到错误消息。这是我的代码:
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import datetime
import statistics
(train_images, train_labels), (test_images, test_labels) = np.load("dataset.npy", allow_pickle=True)
train_images = train_images / 255
test_images = test_images / 255
train_labels = list(map(float, train_labels))
test_labels = list(map(float, test_labels))
train_labels = [i/10 for i in train_labels]
test_labels = [i/10 for i in test_labels]
'''
model = keras.Sequential([
keras.layers.Flatten(input_shape=(128, 128)),
keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dense(1)
])
'''
start_time = datetime.datetime.now()
model = keras.Sequential([
keras.layers.Conv2D(32, kernel_size=(5, 5), strides=(1, 1), activation='relu', input_shape=(128, 128, 1)),
keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Conv2D(64, (5, 5), activation='relu'),
keras.layers.MaxPooling2D(pool_size=(2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Flatten(),
keras.layers.Dropout(0.5),
keras.layers.Dense(1000, activation='relu'),
keras.layers.Dense(1)
])
model.compile(loss='mean_absolute_error',
optimizer=keras.optimizers.SGD(lr=0.01),
metrics=['mean_absolute_error', 'mean_squared_error'])
train_images = train_images.reshape(328, 128, 128, 1)
test_images = test_images.reshape(82, 128, 128, 1)
model.fit(train_images, train_labels, epochs=100, callbacks=[keras.callbacks.ModelCheckpoint("cp.ckpt", monitor='mean_absolute_error', save_best_only=True, verbose=1)])
model.load_weights("cp.ckpt")
predictions = model.predict(test_images)
totalDifference = 0
for i in range(82):
print("%s: %s" % (test_labels[i] * 10, predictions[i] * 10))
totalDifference += abs(test_labels[i] - predictions[i])
avgDifference = totalDifference / 8.2
print("\n%s\n" % avgDifference)
print("Time Elapsed:")
print(datetime.datetime.now() - start_time)
答案 0 :(得分:3)
TLDR;您正在保存整个模型,而仅尝试加载权重,这不是它的工作原理。
您模型的fit
:
model.fit(
train_images,
train_labels,
epochs=100,
callbacks=[
keras.callbacks.ModelCheckpoint(
"cp.ckpt", monitor="mean_absolute_error", save_best_only=True, verbose=1
)
],
)
默认情况下,save_weights=False
中的ModelCheckpoint
会将整个模型保存到.ckpt
。
顺便说一句。文件应命名为Hierarchical Data Format 5
,.hdf5
或.hf5
。由于Windows与扩展名无关,如果tensorflow
/ keras
依赖于此OS上的扩展名,则可能会遇到一些问题。
另一方面,您仅加载模型的权重,而文件包含整个模型:
model.load_weights("cp.ckpt")
Tensorflow的检查点(.cp
)机制与Keras的(.hdf5
)不同,因此请当心(有计划将它们更紧密地集成,请参见here和{{3 }}。
因此,或者像现在一样使用回调,但是使用model.load("model.hdf5")
或向save_weights_only=True
添加ModelCheckpoint
参数:
model.fit(
train_images,
train_labels,
epochs=100,
callbacks=[
keras.callbacks.ModelCheckpoint(
"weights.hdf5",
monitor="mean_absolute_error",
save_best_only=True,
verbose=1,
save_weights_only=True, # Specify this
)
],
)
,您就可以使用model.load_weights("weights.hdf5")
。
答案 1 :(得分:1)
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(tf.random_normal([784, 200], stddev=0.35), name="v1")
v2 = tf.Variable(tf.random_normal([784, 200], stddev=0.35), name="v2")
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
# Do some work with the model