吴恩达深度神经网络笔记—搭建卷积神经网络模型（TensorFlow）

利用框架搭建卷积神经网络模型

导包

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *

%matplotlib inline
np.random.seed(1)

加载数据集

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

数据集是6个符号的集合，表示从0到5的数字。

查看数据集中一条数据

index = 15
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))

结果：

数据集处理

X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
conv_layers = {
   }

结果：

number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)

创建占位符

def create_placeholders(n_H0, n_W0, n_C0, n_y):
   
    X = tf.compat.v1.placeholder(tf.float32,[None, n_H0, n_W0, n_C0])
    Y = tf.compat.v1.placeholder(tf.float32,[None, n_y])
    
    return X, Y

为什么要使用tf.placeholder?
因为每一个tensor在graph上都是一个op。当我们将train数据分成一个个minibatch然后传入网络进行训练时，每一个minibatch都将是一个op，这样的话，一副graph上的op未免太多，也会产生巨大的开销；于是就有了tf.placeholder，我们每次可以将 一个minibatch传入到x = tf.placeholder(tf.float32,[None,32])上，下一次传入的x都替换掉上一次传入的x，这样就对于所有传入的minibatch x就只会产生一个op，不会产生其他多余的op，进而减少了graph的开销。

测试：

X, Y = create_placeholders(64, 64, 3, 6)
print ("X = " + str(X