0. 安装流程
- 首先安装 Anaconda；
- 然后安装 cuda, cudnn；注意：cuda编译安装很慢并且不方便；
conda install cudatoolkit=10.1
conda install cudnn=7.6.5
pip install tensorflow-gpu==2.3.0 -i https://pypi.douban.com/simple
1. 基本操作
2. 数据管理
2.1 加载&解析数据
数据格式.png
2.2 TFRecord数据格式
spark tfrecord举例.png
3. 模型管理
# Save weights and optimizer variables.
# Create a dict of variables to save.
vars_to_save = {"W": W, "b": b, "optimizer": optimizer}
# TF Checkpoint, pass the dict as **kwargs.
checkpoint = tf.train.Checkpoint(**vars_to_save)
# TF CheckpointManager to manage saving parameters.
saver = tf.train.CheckpointManager(
checkpoint, directory="./tf-example", max_to_keep=5)
# Save variables.
saver.save()
# Set checkpoint to load data.
vars_to_load = {"W": W, "b": b, "optimizer": optimizer}
checkpoint = tf.train.Checkpoint(**vars_to_load)
# Restore variables from latest checkpoint.
latest_ckpt = tf.train.latest_checkpoint("./tf-example")
checkpoint.restore(latest_ckpt)
from tensorflow.keras import Model, layers
# Create TF Model.
class NeuralNet(Model):
# Set layers.
def __init__(self):
super(NeuralNet, self).__init__(name="NeuralNet")
# First fully-connected hidden layer.
self.fc1 = layers.Dense(64, activation=tf.nn.relu)
# Second fully-connected hidden layer.
self.fc2 = layers.Dense(128, activation=tf.nn.relu)
# Third fully-connecter hidden layer.
self.out = layers.Dense(num_classes, activation=tf.nn.softmax)
# Set forward pass.
def __call__(self, x, is_training=False):
x = self.fc1(x)
x = self.out(x)
if not is_training:
# tf cross entropy expect logits without softmax, so only
# apply softmax when not training.
x = tf.nn.softmax(x)
return x
# Build neural network model.
neural_net = NeuralNet()
# 模型训练...
# Save TF model.
neural_net.save_weights(filepath="./tfmodel.ckpt")
# Load saved weights.
neural_net.load_weights(filepath="./tfmodel.ckpt")
4. 自定义layers, modules
4.1 自定义layers
- 自定义layer类必须实现：
__init__, build, call 三个方法； build方法用于定义本层使用的网络参数；call方法用于定义本层的前向传播过程；get_config是可选的；
# Create a custom layer, extending TF 'Layer' class.
# Layer compute: y = relu(W * x + b)
class CustomLayer1(layers.Layer):
# Layer arguments.
def __init__(self, num_units, **kwargs):
# Store the number of units (neurons).
self.num_units = num_units
super(CustomLayer1, self).__init__(**kwargs)
def build(self, input_shape):
# Note: a custom layer can also include any other TF 'layers'.
shape = tf.TensorShape((input_shape[1], self.num_units))
# Create weight variables for this layer.
self.weight = self.add_weight(name='W',
shape=shape,
initializer=tf.initializers.RandomNormal,
trainable=True)
self.bias = self.add_weight(name='b',
shape=[self.num_units])
# Make sure to call the `build` method at the end
super(CustomLayer1, self).build(input_shape)
def call(self, inputs):
x = tf.matmul(inputs, self.weight)
x = x + self.bias
return tf.nn.relu(x)
def get_config(self):
base_config = super(CustomLayer1, self).get_config()
base_config['num_units'] = self.num_units
return base_config
4.2 自定义modules
- 自定义module类必须实现两个方法：
__init__, __call__方法；__call__方法用于定义模型前向传播过程；
# Create TF Model.
class CustomNet(Model):
def __init__(self):
super(CustomNet, self).__init__()
# Use custom layers created above.
self.layer1 = CustomLayer1(64)
self.layer2 = CustomLayer2(64)
self.out = layers.Dense(num_classes, activation=tf.nn.softmax)
# Set forward pass.
def __call__(self, x, is_training=False):
x = self.layer1(x)
x = tf.nn.relu(x)
x = self.layer2(x)
if not is_training:
# tf cross entropy expect logits without softmax, so only
# apply softmax when not training.
x = tf.nn.softmax(x)
return x
# Build neural network model.
custom_net = CustomNet()
4.3 自定义损失函数、评价指标、训练过程
- 自定义损失函数，cross_entropy交叉熵损失函数；评价指标accuracy；
# Cross-Entropy loss function.
def cross_entropy(y_pred, y_true):
y_true = tf.cast(y_true, tf.int64)
crossentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)
return tf.reduce_mean(crossentropy)
# Accuracy metric.
def accuracy(y_pred, y_true):
# Predicted class is the index of highest score in prediction vector (i.e. argmax).
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Adam optimizer.
optimizer = tf.optimizers.Adam(learning_rate)
# Optimization process.
def run_optimization(x, y):
# Wrap computation inside a GradientTape for automatic differentiation.
with tf.GradientTape() as g:
pred = custom_net(x, is_training=True)
loss = cross_entropy(pred, y)
# Compute gradients.
gradients = g.gradient(loss, custom_net.trainable_variables)
# Update W and b following gradients.
optimizer.apply_gradients(zip(gradients, custom_net.trainable_variables))
# Run training for the given number of steps.
for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):
# Run the optimization to update W and b values.
run_optimization(batch_x, batch_y)
if step % display_step == 0:
pred = custom_net(batch_x, is_training=False)
loss = cross_entropy(pred, batch_y)
acc = accuracy(pred, batch_y)
print("step: %i, loss: %f, accuracy: %f" % (step, loss, acc))
5. TensorBoard可视化
https://github.com/nlpming/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb
6. 模型实现例子
6.1 基础模型
6.2 前馈神经网络
6.3 卷积神经网络
6.4 循环神经网络
6.5 非监督式算法
参考资料
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