一看就懂的Tensorflow实战(多层感知机模型Eager API)
时间:2022-07-22
本文章向大家介绍一看就懂的Tensorflow实战(多层感知机模型Eager API),主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。
Tensorflow多层感知机Eager API
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.eager as tfe
设置 Eager API
# Set Eager API
tfe.enable_eager_execution()
导入数据集
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./data/", one_hot=False)
Extracting ./data/train-images-idx3-ubyte.gz
Extracting ./data/train-labels-idx1-ubyte.gz
Extracting ./data/t10k-images-idx3-ubyte.gz
Extracting ./data/t10k-labels-idx1-ubyte.gz
定义参数
# Parameters
learning_rate = 0.001
num_steps = 1000
batch_size = 128
display_step = 100
# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)
数据拆分成批
# Using TF Dataset to split data into batches
dataset = tf.data.Dataset.from_tensor_slices(
(mnist.train.images, mnist.train.labels)).batch(batch_size)
dataset_iter = tfe.Iterator(dataset)
定义多层感知机模型
# Define the neural network. To use eager API and tf.layers API together,
# we must instantiate a tfe.Network class as follow:
class NeuralNet(tfe.Network):
def __init__(self):
# Define each layer
super(NeuralNet, self).__init__()
# Hidden fully connected layer with 256 neurons
self.layer1 = self.track_layer(
tf.layers.Dense(n_hidden_1, activation=tf.nn.relu))
# Hidden fully connected layer with 256 neurons
self.layer2 = self.track_layer(
tf.layers.Dense(n_hidden_2, activation=tf.nn.relu))
# Output fully connected layer with a neuron for each class
self.out_layer = self.track_layer(tf.layers.Dense(num_classes))
def call(self, x):
x = self.layer1(x)
x = self.layer2(x)
return self.out_layer(x)
neural_net = NeuralNet()
定义损失函数+优化方法+准确率
# Cross-Entropy loss function
def loss_fn(inference_fn, inputs, labels):
# Using sparse_softmax cross entropy
return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=inference_fn(inputs), labels=labels))
# Calculate accuracy
def accuracy_fn(inference_fn, inputs, labels):
prediction = tf.nn.softmax(inference_fn(inputs))
correct_pred = tf.equal(tf.argmax(prediction, 1), labels)
return tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# SGD Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# Compute gradients
grad = tfe.implicit_gradients(loss_fn)
训练
# Training
average_loss = 0.
average_acc = 0.
for step in range(num_steps):
# Iterate through the dataset
try:
d = dataset_iter.next()
except StopIteration:
# Refill queue
dataset_iter = tfe.Iterator(dataset)
d = dataset_iter.next()
# Images
x_batch = d[0]
# Labels
y_batch = tf.cast(d[1], dtype=tf.int64)
# Compute the batch loss
batch_loss = loss_fn(neural_net, x_batch, y_batch)
average_loss += batch_loss
# Compute the batch accuracy
batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch)
average_acc += batch_accuracy
if step == 0:
# Display the initial cost, before optimizing
print("Initial loss= {:.9f}".format(average_loss))
# Update the variables following gradients info
optimizer.apply_gradients(grad(neural_net, x_batch, y_batch))
# Display info
if (step + 1) % display_step == 0 or step == 0:
if step > 0:
average_loss /= display_step
average_acc /= display_step
print("Step:", '%04d' % (step + 1), " loss=",
"{:.9f}".format(average_loss), " accuracy=",
"{:.4f}".format(average_acc))
average_loss = 0.
average_acc = 0.
Initial loss= 2.362281322
Step: 0001 loss= 2.362281322 accuracy= 0.0391
Step: 0100 loss= 0.583163500 accuracy= 0.8291
Step: 0200 loss= 0.247603565 accuracy= 0.9281
Step: 0300 loss= 0.214451462 accuracy= 0.9360
Step: 0400 loss= 0.182251021 accuracy= 0.9452
Step: 0500 loss= 0.139149994 accuracy= 0.9585
Step: 0600 loss= 0.120601922 accuracy= 0.9649
Step: 0700 loss= 0.114957660 accuracy= 0.9655
Step: 0800 loss= 0.111238368 accuracy= 0.9660
Step: 0900 loss= 0.085549861 accuracy= 0.9754
Step: 1000 loss= 0.079464689 accuracy= 0.9752
测试评估
# Evaluate model on the test image set
testX = mnist.test.images
testY = mnist.test.labels
test_acc = accuracy_fn(neural_net, testX, testY)
print("Testset Accuracy: {:.4f}".format(test_acc))
Testset Accuracy: 0.9707
参考
[TensorFlow-Examples]https://github.com/aymericdamien/TensorFlow-Examples
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