TensorFlow学习报告

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 11 19:10:39 2022

@author: 10320
"""

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
train_images = train_images / 255.0

test_images = test_images / 255.0

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

　　

 

 

（1）TensorFlow和PyTorch

（2）可以直接赋值，也可以使用初始化函数

import tensorflow as tf

bias1=tf.Variable(2)

bias2=tf.Variable(initial_value=3.)

 

还有其他更加复杂的初始化方法 如：tf.zeros\tf.zeros_like\tf.ones_like\tf.random.truncated_normal等等

tf.random.truncated_normal和tf.zeros是常常用来进行权值和偏置的初始化方法

（3）序贯式、函数式

 

#序贯式1
import tensorflow as tf

model = tf.keras.Sequential()
#创建一个全连接层，神经元个数为256，输入为784，激活函数为relu
model.add(tf.keras.layers.Dense(256, activation='relu', input_dim=784))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

#序贯式2
import tensorflow as tf

imput_layer = tf.keras.layers.Input(shape=(784,))
hid1_layer = tf.keras.layers.Dense(256, activation='relu')
hid2_layer = tf.keras.layers.Dense(128, activation='relu')
output_layers = tf.keras.layers.Dense(10, activation='softmax') #将层的列表传给Sequential的构造函数
model = tf.keras.Sequential(layers=[imput_layer, hid1_layer, hid2_layer, output_layers])

 

#函数式
import tensorflow as tf
#创建一个模型，包含一个输入层和三个全连接层
inputs = tf.keras.layers.Input(shape=(4))
x=tf.keras.layers.Dense(32,activation='relu')(inputs)
x=tf.keras.layers.Dense(64,activation='relu')(x)
outputs=tf.keras.layers.Dense(3,activation='softmax')(x)
model=tf.keras.Model(inputs=inputs,outputs =outputs)

 

（4）

import torch
data=torch.rand(5,3)
print(data)

 

 

（5）Keras、Caffe、MXNet、Sonnet、Deeplearning4j