最近在学习相关Keras的相关内容，看到TensorFlow的动态图的创建内容，根据大牛们的代码修改一下，操作mnist数据集，记录下来，以后好查找，相关的知识点。
加载库
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tf.enable_eager_execution()
print("tensorflow: {}".format(tf.VERSION))
from keras.datasets import mnist
import numpy as np
数据集相关的操作
# 下载数据集
def load_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
return (x_train, y_train), (x_test, y_test)
# 处理相关的数据集
class MnistData:
def __init__(self, name, need_shuffle):
(x_train, y_train), (x_test, y_test) = load_data()
# 训练集和测试集的处理方法
if name == 'train':
# 数据归一化 0~1
self._data = x_train
self._labels = y_train
else:
# 数据归一化 0~1
self._data = x_test
self._labels = y_test
print(self._data.shape)
print(self._labels.shape)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0
if self._need_shuffle:
self._shuffle_data()
# 打乱数据集
def _shuffle_data(self):
# [0,1,2,3,4,5] -> [5,3,2,4,0,1]
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
# 获取batchsize的数据
def next_batch(self, batch_size):
"""return batch_size examples as a batch."""
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator: end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_data, batch_labels
获取数据
train_data = MnistData('train', True)
batch_size = 20
创建模型 使用tf.layers.Layer
# 创建模型mnist，init方法，是定义相关的操作，call方法，是把init方法组合起来
class MNISTModel(tf.layers.Layer):
def __init__(self, name):
super(MNISTModel, self).__init__(name=name)
self._input_shape = [-1, 28, 28, 1]
self.conv1 = tf.layers.Conv2D(32, 5, activation=tf.nn.relu)
self.conv2 = tf.layers.Conv2D(64, 5, activation=tf.nn.relu)
self.fc1 = tf.layers.Dense(1024, activation=tf.nn.relu)
self.fc2 = tf.layers.Dense(10)
self.dropout = tf.layers.Dropout(0.5)
self.max_pool2d = tf.layers.MaxPooling2D(
(2, 2), (2, 2), padding='SAME')
def call(self, inputs, training):
x = tf.reshape(inputs, self._input_shape)
x = self.conv1(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.max_pool2d(x)
x = tf.keras.layers.Flatten()(x)
x = self.fc1(x)
if training:
x = self.dropout(x)
x = self.fc2(x)
return x
创建loss函数
def loss(model, inputs, labels):
predictions = model(inputs, training=True)
print(predictions[2])
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=predictions, labels=labels)
return tf.reduce_sum(cost)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
grad = tfe.implicit_gradients(loss)
训练相关代码
model = MNISTModel('net')
global_step = tf.train.get_or_create_global_step()
for epoch in range(5):
for i in range(6000 // batch_size):
inputs, labels = train_data.next_batch(batch_size)
inputs, labels = tf.cast(inputs, tf.float32), tf.cast(labels, tf.int32)
optimizer.apply_gradients(grad(model, inputs, labels), global_step=global_step)
if i % 50 == 0:
print("Step %d: Loss on training set : %f" %
(i, loss(model, inputs, labels).numpy()))
all_variables = (
model.variables
+ optimizer.variables()
+ [global_step])
tfe.Saver(all_variables).save(
"./log/ministmodel.cpkt", global_step=global_step)
test_data = MnistData('test', False)
test_inputs, test_labels = test_data.next_batch(100)
test_inputs, test_labels = tf.cast(test_inputs, tf.float32), tf.cast(test_labels, tf.int32)
print("Loss on test set : %f" %
(loss(model, test_inputs, test_labels).numpy()))