如何使用 TFRecord 数据集使 TensorFlow + Keras 快速运行？
How do you make TensorFlow + Keras fast with a TFRecord dataset?
如何将 TensorFlow TFRecord 与 Keras 模型和 tf.session.run() 结合使用，同时将数据集保持在带有队列运行器的张量中？
下面是一个有效的代码片段，但它需要进行以下改进：
- 使用Model API
- 指定输入()
- 从 TFRecord 加载数据集
- 运行 并行处理数据集（例如使用 queuerunner）
这是代码片段，有几行 TODO 行指示需要什么：
from keras.models import Model
import tensorflow as tf
from keras import backend as K
from keras.layers import Dense, Input
from keras.objectives import categorical_crossentropy
from tensorflow.examples.tutorials.mnist import input_data
sess = tf.Session()
K.set_session(sess)
# Can this be done more efficiently than placeholders w/ TFRecords?
img = tf.placeholder(tf.float32, shape=(None, 784))
labels = tf.placeholder(tf.float32, shape=(None, 10))
# TODO: Use Input()
x = Dense(128, activation='relu')(img)
x = Dense(128, activation='relu')(x)
preds = Dense(10, activation='softmax')(x)
# TODO: Construct model = Model(input=inputs, output=preds)
loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# TODO: handle TFRecord data, is it the same?
mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
sess.run(tf.global_variables_initializer())
# TODO remove default, add queuerunner
with sess.as_default():
for i in range(1000):
batch = mnist_data.train.next_batch(50)
train_step.run(feed_dict={img: batch[0],
labels: batch[1]})
print(loss.eval(feed_dict={img: mnist_data.test.images,
labels: mnist_data.test.labels}))
为什么这个问题是相关的？
- 无需回到 python 的高性能训练
- 没有
张量转换
- Keras will soon be part of tensorflow
- 演示 Keras Model() 类 如何正确接受输入数据的张量。
这是语义分割问题示例的一些入门信息：
- 示例 unet Keras 模型 unet.py，恰好用于语义分割。
- Keras + Tensorflow Blog Post
- 一个attempt at running the unet model a tf session with TFRecords and a Keras model（不工作）
- 创建 TFRecords 的代码：tf_records.py
- 尝试 运行 unet 模型与 TFRecords 的 tf 会话和 Keras 模型 densenet_fcn.py（不工作）
我不使用 tfrecord 数据集格式，因此不会争论优缺点，但我对扩展 Keras 以支持相同格式很感兴趣。
github.com/indraforyou/keras_tfrecord 是存储库。将简要说明主要变化。
Dataset creation and loading
data_to_tfrecord 和 read_and_decode here 负责创建 tfrecord 数据集并加载它们。实施 read_and_decode 时必须特别小心，否则您将在训练期间遇到神秘错误。
Initialization and Keras model
现在 tf.train.shuffle_batch 和 Keras Input 层 returns 张量。但是 tf.train.shuffle_batch 返回的那个没有 Keras 内部需要的元数据。事实证明，通过使用 tensor 参数调用 Input 层，任何张量都可以很容易地转换为带有 keras 元数据的张量。
所以这负责初始化：
x_train_, y_train_ = ktfr.read_and_decode('train.mnist.tfrecord', one_hot=True, n_class=nb_classes, is_train=True)
x_train_batch, y_train_batch = K.tf.train.shuffle_batch([x_train_, y_train_],
batch_size=batch_size,
capacity=2000,
min_after_dequeue=1000,
num_threads=32) # set the number of threads here
x_train_inp = Input(tensor=x_train_batch)
现在 x_train_inp 可以开发任何 keras 模型。
Training (simple)
假设 train_out 是您的 keras 模型的输出张量。您可以轻松地在以下行中编写自定义训练循环：
loss = tf.reduce_mean(categorical_crossentropy(y_train_batch, train_out))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
# sess.run(tf.global_variables_initializer())
sess.run(tf.initialize_all_variables())
with sess.as_default():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss], feed_dict={K.learning_phase(): 0})
duration = time.time() - start_time
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
finally:
coord.request_stop()
coord.join(threads)
sess.close()
Training (keras style)
keras 如此有利可图的一个特点是它具有回调函数的通用训练机制。
但是为了支持 tfrecords 类型的训练，fit 函数需要做几处更改
- 运行宁队列线程
- 没有通过
feed_dict 批量输入数据
- 支持验证变得棘手，因为验证数据也将通过另一个张量进入，需要在内部创建一个不同的模型，共享上层和由其他 tfrecord reader 提供的验证张量。
但这一切都可以通过另一个标志参数轻松支持。让事情变得混乱的是 keras 特征 sample_weight 和 class_weight 它们用于对每个样本进行称重并称重每个 class。为此，在 compile() keras 中创建了占位符 (here) and placeholders are also implicitly created for the targets (here)，这在我们的例子中不需要，标签已经由 tfrecord readers 提供。这些占位符需要在会话 运行 期间输入，这在我们的 cae 中是不必要的。
因此，考虑到这些更改，compile_tfrecord(here) and fit_tfrecord(here) 是 compile 和 fit 的扩展，并且共享了 95% 的代码。
它们可以通过以下方式使用：
import keras_tfrecord as ktfr
train_model = Model(input=x_train_inp, output=train_out)
ktfr.compile_tfrecord(train_model, optimizer='rmsprop', loss='categorical_crossentropy', out_tensor_lst=[y_train_batch], metrics=['accuracy'])
train_model.summary()
ktfr.fit_tfrecord(train_model, X_train.shape[0], batch_size, nb_epoch=3)
train_model.save_weights('saved_wt.h5')
欢迎您改进代码和拉取请求。
更新2018-08-29 keras现在直接支持这个，看下面的例子：
https://github.com/keras-team/keras/blob/master/examples/mnist_tfrecord.py
原答案：
TFRecords 通过使用外部损失来支持。以下是构造外部损失的关键行：
# tf yield ops that supply dataset images and labels
x_train_batch, y_train_batch = read_and_decode_recordinput(...)
# create a basic cnn
x_train_input = Input(tensor=x_train_batch)
x_train_out = cnn_layers(x_train_input)
model = Model(inputs=x_train_input, outputs=x_train_out)
loss = keras.losses.categorical_crossentropy(y_train_batch, x_train_out)
model.add_loss(loss)
model.compile(optimizer='rmsprop', loss=None)
这是 Keras 2 的示例。应用小补丁 #7060:
后它可以工作
'''MNIST dataset with TensorFlow TFRecords.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
'''
import os
import copy
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import data_flow_ops
from keras import backend as K
from keras.models import Model
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import EarlyStopping
from keras.callbacks import TensorBoard
from keras.objectives import categorical_crossentropy
from keras.utils import np_utils
from keras.utils.generic_utils import Progbar
from keras import callbacks as cbks
from keras import optimizers, objectives
from keras import metrics as metrics_module
from keras.datasets import mnist
if K.backend() != 'tensorflow':
raise RuntimeError('This example can only run with the '
'TensorFlow backend for the time being, '
'because it requires TFRecords, which '
'are not supported on other platforms.')
def images_to_tfrecord(images, labels, filename):
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
""" Save data into TFRecord """
if not os.path.isfile(filename):
num_examples = images.shape[0]
rows = images.shape[1]
cols = images.shape[2]
depth = images.shape[3]
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
image_raw = images[index].tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(rows),
'width': _int64_feature(cols),
'depth': _int64_feature(depth),
'label': _int64_feature(int(labels[index])),
'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
writer.close()
else:
print('tfrecord %s already exists' % filename)
def read_and_decode_recordinput(tf_glob, one_hot=True, classes=None, is_train=None,
batch_shape=[1000, 28, 28, 1], parallelism=1):
""" Return tensor to read from TFRecord """
print 'Creating graph for loading %s TFRecords...' % tf_glob
with tf.variable_scope("TFRecords"):
record_input = data_flow_ops.RecordInput(
tf_glob, batch_size=batch_shape[0], parallelism=parallelism)
records_op = record_input.get_yield_op()
records_op = tf.split(records_op, batch_shape[0], 0)
records_op = [tf.reshape(record, []) for record in records_op]
progbar = Progbar(len(records_op))
images = []
labels = []
for i, serialized_example in enumerate(records_op):
progbar.update(i)
with tf.variable_scope("parse_images", reuse=True):
features = tf.parse_single_example(
serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
})
img = tf.decode_raw(features['image_raw'], tf.uint8)
img.set_shape(batch_shape[1] * batch_shape[2])
img = tf.reshape(img, [1] + batch_shape[1:])
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
label = tf.cast(features['label'], tf.int32)
if one_hot and classes:
label = tf.one_hot(label, classes)
images.append(img)
labels.append(label)
images = tf.parallel_stack(images, 0)
labels = tf.parallel_stack(labels, 0)
images = tf.cast(images, tf.float32)
images = tf.reshape(images, shape=batch_shape)
# StagingArea will store tensors
# across multiple steps to
# speed up execution
images_shape = images.get_shape()
labels_shape = labels.get_shape()
copy_stage = data_flow_ops.StagingArea(
[tf.float32, tf.float32],
shapes=[images_shape, labels_shape])
copy_stage_op = copy_stage.put(
[images, labels])
staged_images, staged_labels = copy_stage.get()
return images, labels
def save_mnist_as_tfrecord():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
images_to_tfrecord(images=X_train, labels=y_train, filename='train.mnist.tfrecord')
images_to_tfrecord(images=X_test, labels=y_test, filename='test.mnist.tfrecord')
def cnn_layers(x_train_input):
x = Conv2D(32, (3, 3), activation='relu', padding='valid')(x_train_input)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x_train_out = Dense(classes,
activation='softmax',
name='x_train_out')(x)
return x_train_out
sess = tf.Session()
K.set_session(sess)
save_mnist_as_tfrecord()
batch_size = 100
batch_shape = [batch_size, 28, 28, 1]
epochs = 3000
classes = 10
parallelism = 10
x_train_batch, y_train_batch = read_and_decode_recordinput(
'train.mnist.tfrecord',
one_hot=True,
classes=classes,
is_train=True,
batch_shape=batch_shape,
parallelism=parallelism)
x_test_batch, y_test_batch = read_and_decode_recordinput(
'test.mnist.tfrecord',
one_hot=True,
classes=classes,
is_train=True,
batch_shape=batch_shape,
parallelism=parallelism)
x_batch_shape = x_train_batch.get_shape().as_list()
y_batch_shape = y_train_batch.get_shape().as_list()
x_train_input = Input(tensor=x_train_batch, batch_shape=x_batch_shape)
x_train_out = cnn_layers(x_train_input)
y_train_in_out = Input(tensor=y_train_batch, batch_shape=y_batch_shape, name='y_labels')
cce = categorical_crossentropy(y_train_batch, x_train_out)
train_model = Model(inputs=[x_train_input], outputs=[x_train_out])
train_model.add_loss(cce)
train_model.compile(optimizer='rmsprop',
loss=None,
metrics=['accuracy'])
train_model.summary()
tensorboard = TensorBoard()
# tensorboard disabled due to Keras bug
train_model.fit(batch_size=batch_size,
epochs=epochs) # callbacks=[tensorboard])
train_model.save_weights('saved_wt.h5')
K.clear_session()
# Second Session, pure Keras
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
x_test_inp = Input(batch_shape=(None,) + (X_test.shape[1:]))
test_out = cnn_layers(x_test_inp)
test_model = Model(inputs=x_test_inp, outputs=test_out)
test_model.load_weights('saved_wt.h5')
test_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
test_model.summary()
loss, acc = test_model.evaluate(X_test, np_utils.to_categorical(y_test), classes)
print('\nTest accuracy: {0}'.format(acc))
我也一直致力于在以下问题和拉取请求中改进对 TFRecords 的支持：
终于可以使用tf.contrib.learn.Experiment在TensorFlow中训练Keras模型了。
如何将 TensorFlow TFRecord 与 Keras 模型和 tf.session.run() 结合使用，同时将数据集保持在带有队列运行器的张量中？
下面是一个有效的代码片段，但它需要进行以下改进：
- 使用Model API
- 指定输入()
- 从 TFRecord 加载数据集
- 运行 并行处理数据集（例如使用 queuerunner）
这是代码片段，有几行 TODO 行指示需要什么：
from keras.models import Model
import tensorflow as tf
from keras import backend as K
from keras.layers import Dense, Input
from keras.objectives import categorical_crossentropy
from tensorflow.examples.tutorials.mnist import input_data
sess = tf.Session()
K.set_session(sess)
# Can this be done more efficiently than placeholders w/ TFRecords?
img = tf.placeholder(tf.float32, shape=(None, 784))
labels = tf.placeholder(tf.float32, shape=(None, 10))
# TODO: Use Input()
x = Dense(128, activation='relu')(img)
x = Dense(128, activation='relu')(x)
preds = Dense(10, activation='softmax')(x)
# TODO: Construct model = Model(input=inputs, output=preds)
loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# TODO: handle TFRecord data, is it the same?
mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
sess.run(tf.global_variables_initializer())
# TODO remove default, add queuerunner
with sess.as_default():
for i in range(1000):
batch = mnist_data.train.next_batch(50)
train_step.run(feed_dict={img: batch[0],
labels: batch[1]})
print(loss.eval(feed_dict={img: mnist_data.test.images,
labels: mnist_data.test.labels}))
为什么这个问题是相关的？
- 无需回到 python 的高性能训练
- 没有
- 没有
- Keras will soon be part of tensorflow
- 演示 Keras Model() 类 如何正确接受输入数据的张量。
这是语义分割问题示例的一些入门信息：
- 示例 unet Keras 模型 unet.py，恰好用于语义分割。
- Keras + Tensorflow Blog Post
- 一个attempt at running the unet model a tf session with TFRecords and a Keras model（不工作）
- 创建 TFRecords 的代码：tf_records.py
- 尝试 运行 unet 模型与 TFRecords 的 tf 会话和 Keras 模型 densenet_fcn.py（不工作）
我不使用 tfrecord 数据集格式，因此不会争论优缺点，但我对扩展 Keras 以支持相同格式很感兴趣。
github.com/indraforyou/keras_tfrecord 是存储库。将简要说明主要变化。
Dataset creation and loading
data_to_tfrecord 和 read_and_decode here 负责创建 tfrecord 数据集并加载它们。实施 read_and_decode 时必须特别小心，否则您将在训练期间遇到神秘错误。
Initialization and Keras model
现在 tf.train.shuffle_batch 和 Keras Input 层 returns 张量。但是 tf.train.shuffle_batch 返回的那个没有 Keras 内部需要的元数据。事实证明，通过使用 tensor 参数调用 Input 层，任何张量都可以很容易地转换为带有 keras 元数据的张量。
所以这负责初始化：
x_train_, y_train_ = ktfr.read_and_decode('train.mnist.tfrecord', one_hot=True, n_class=nb_classes, is_train=True)
x_train_batch, y_train_batch = K.tf.train.shuffle_batch([x_train_, y_train_],
batch_size=batch_size,
capacity=2000,
min_after_dequeue=1000,
num_threads=32) # set the number of threads here
x_train_inp = Input(tensor=x_train_batch)
现在 x_train_inp 可以开发任何 keras 模型。
Training (simple)
假设 train_out 是您的 keras 模型的输出张量。您可以轻松地在以下行中编写自定义训练循环：
loss = tf.reduce_mean(categorical_crossentropy(y_train_batch, train_out))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
# sess.run(tf.global_variables_initializer())
sess.run(tf.initialize_all_variables())
with sess.as_default():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
_, loss_value = sess.run([train_op, loss], feed_dict={K.learning_phase(): 0})
duration = time.time() - start_time
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
finally:
coord.request_stop()
coord.join(threads)
sess.close()
Training (keras style)
keras 如此有利可图的一个特点是它具有回调函数的通用训练机制。
但是为了支持 tfrecords 类型的训练，fit 函数需要做几处更改
- 运行宁队列线程
- 没有通过
feed_dict批量输入数据
- 支持验证变得棘手，因为验证数据也将通过另一个张量进入，需要在内部创建一个不同的模型，共享上层和由其他 tfrecord reader 提供的验证张量。
但这一切都可以通过另一个标志参数轻松支持。让事情变得混乱的是 keras 特征 sample_weight 和 class_weight 它们用于对每个样本进行称重并称重每个 class。为此，在 compile() keras 中创建了占位符 (here) and placeholders are also implicitly created for the targets (here)，这在我们的例子中不需要，标签已经由 tfrecord readers 提供。这些占位符需要在会话 运行 期间输入，这在我们的 cae 中是不必要的。
因此，考虑到这些更改，compile_tfrecord(here) and fit_tfrecord(here) 是 compile 和 fit 的扩展，并且共享了 95% 的代码。
它们可以通过以下方式使用：
import keras_tfrecord as ktfr
train_model = Model(input=x_train_inp, output=train_out)
ktfr.compile_tfrecord(train_model, optimizer='rmsprop', loss='categorical_crossentropy', out_tensor_lst=[y_train_batch], metrics=['accuracy'])
train_model.summary()
ktfr.fit_tfrecord(train_model, X_train.shape[0], batch_size, nb_epoch=3)
train_model.save_weights('saved_wt.h5')
欢迎您改进代码和拉取请求。
更新2018-08-29 keras现在直接支持这个，看下面的例子：
https://github.com/keras-team/keras/blob/master/examples/mnist_tfrecord.py
原答案：
TFRecords 通过使用外部损失来支持。以下是构造外部损失的关键行：
# tf yield ops that supply dataset images and labels
x_train_batch, y_train_batch = read_and_decode_recordinput(...)
# create a basic cnn
x_train_input = Input(tensor=x_train_batch)
x_train_out = cnn_layers(x_train_input)
model = Model(inputs=x_train_input, outputs=x_train_out)
loss = keras.losses.categorical_crossentropy(y_train_batch, x_train_out)
model.add_loss(loss)
model.compile(optimizer='rmsprop', loss=None)
这是 Keras 2 的示例。应用小补丁 #7060:
后它可以工作'''MNIST dataset with TensorFlow TFRecords.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
'''
import os
import copy
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import data_flow_ops
from keras import backend as K
from keras.models import Model
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import EarlyStopping
from keras.callbacks import TensorBoard
from keras.objectives import categorical_crossentropy
from keras.utils import np_utils
from keras.utils.generic_utils import Progbar
from keras import callbacks as cbks
from keras import optimizers, objectives
from keras import metrics as metrics_module
from keras.datasets import mnist
if K.backend() != 'tensorflow':
raise RuntimeError('This example can only run with the '
'TensorFlow backend for the time being, '
'because it requires TFRecords, which '
'are not supported on other platforms.')
def images_to_tfrecord(images, labels, filename):
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
""" Save data into TFRecord """
if not os.path.isfile(filename):
num_examples = images.shape[0]
rows = images.shape[1]
cols = images.shape[2]
depth = images.shape[3]
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
image_raw = images[index].tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': _int64_feature(rows),
'width': _int64_feature(cols),
'depth': _int64_feature(depth),
'label': _int64_feature(int(labels[index])),
'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
writer.close()
else:
print('tfrecord %s already exists' % filename)
def read_and_decode_recordinput(tf_glob, one_hot=True, classes=None, is_train=None,
batch_shape=[1000, 28, 28, 1], parallelism=1):
""" Return tensor to read from TFRecord """
print 'Creating graph for loading %s TFRecords...' % tf_glob
with tf.variable_scope("TFRecords"):
record_input = data_flow_ops.RecordInput(
tf_glob, batch_size=batch_shape[0], parallelism=parallelism)
records_op = record_input.get_yield_op()
records_op = tf.split(records_op, batch_shape[0], 0)
records_op = [tf.reshape(record, []) for record in records_op]
progbar = Progbar(len(records_op))
images = []
labels = []
for i, serialized_example in enumerate(records_op):
progbar.update(i)
with tf.variable_scope("parse_images", reuse=True):
features = tf.parse_single_example(
serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
})
img = tf.decode_raw(features['image_raw'], tf.uint8)
img.set_shape(batch_shape[1] * batch_shape[2])
img = tf.reshape(img, [1] + batch_shape[1:])
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
label = tf.cast(features['label'], tf.int32)
if one_hot and classes:
label = tf.one_hot(label, classes)
images.append(img)
labels.append(label)
images = tf.parallel_stack(images, 0)
labels = tf.parallel_stack(labels, 0)
images = tf.cast(images, tf.float32)
images = tf.reshape(images, shape=batch_shape)
# StagingArea will store tensors
# across multiple steps to
# speed up execution
images_shape = images.get_shape()
labels_shape = labels.get_shape()
copy_stage = data_flow_ops.StagingArea(
[tf.float32, tf.float32],
shapes=[images_shape, labels_shape])
copy_stage_op = copy_stage.put(
[images, labels])
staged_images, staged_labels = copy_stage.get()
return images, labels
def save_mnist_as_tfrecord():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
images_to_tfrecord(images=X_train, labels=y_train, filename='train.mnist.tfrecord')
images_to_tfrecord(images=X_test, labels=y_test, filename='test.mnist.tfrecord')
def cnn_layers(x_train_input):
x = Conv2D(32, (3, 3), activation='relu', padding='valid')(x_train_input)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x_train_out = Dense(classes,
activation='softmax',
name='x_train_out')(x)
return x_train_out
sess = tf.Session()
K.set_session(sess)
save_mnist_as_tfrecord()
batch_size = 100
batch_shape = [batch_size, 28, 28, 1]
epochs = 3000
classes = 10
parallelism = 10
x_train_batch, y_train_batch = read_and_decode_recordinput(
'train.mnist.tfrecord',
one_hot=True,
classes=classes,
is_train=True,
batch_shape=batch_shape,
parallelism=parallelism)
x_test_batch, y_test_batch = read_and_decode_recordinput(
'test.mnist.tfrecord',
one_hot=True,
classes=classes,
is_train=True,
batch_shape=batch_shape,
parallelism=parallelism)
x_batch_shape = x_train_batch.get_shape().as_list()
y_batch_shape = y_train_batch.get_shape().as_list()
x_train_input = Input(tensor=x_train_batch, batch_shape=x_batch_shape)
x_train_out = cnn_layers(x_train_input)
y_train_in_out = Input(tensor=y_train_batch, batch_shape=y_batch_shape, name='y_labels')
cce = categorical_crossentropy(y_train_batch, x_train_out)
train_model = Model(inputs=[x_train_input], outputs=[x_train_out])
train_model.add_loss(cce)
train_model.compile(optimizer='rmsprop',
loss=None,
metrics=['accuracy'])
train_model.summary()
tensorboard = TensorBoard()
# tensorboard disabled due to Keras bug
train_model.fit(batch_size=batch_size,
epochs=epochs) # callbacks=[tensorboard])
train_model.save_weights('saved_wt.h5')
K.clear_session()
# Second Session, pure Keras
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
x_test_inp = Input(batch_shape=(None,) + (X_test.shape[1:]))
test_out = cnn_layers(x_test_inp)
test_model = Model(inputs=x_test_inp, outputs=test_out)
test_model.load_weights('saved_wt.h5')
test_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
test_model.summary()
loss, acc = test_model.evaluate(X_test, np_utils.to_categorical(y_test), classes)
print('\nTest accuracy: {0}'.format(acc))
我也一直致力于在以下问题和拉取请求中改进对 TFRecords 的支持：
终于可以使用tf.contrib.learn.Experiment在TensorFlow中训练Keras模型了。