前言
学习笔记来自于Andrew Ng,文末附资料。
1 - 导入TensorFlow库
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
import tf_utils
import time
%matplotlib inline
np.random.seed(1)
Exercise----1
=============================
y_hat = tf.constant(36, name='y_hat') #定义y_hat为固定值36
y = tf.constant(39, name='y') #定义y为固定值39
loss = tf.Variable((y-y_hat)**2, name='loss') #为损失函数创建一个变量
init = tf.global_variables_initializer() #运行之后的初始化(ession.run(init)
sess = tf.Session() #损失变量将被初始化并准备计算
sess.run(init) #初始化变量
print(sess.run(loss)) #创建一个session并打印输出
9
占位符是一个对象,它的值只能在稍后指定,要指定占位符的值,可以使用一个feed字典(feed_dict变量)来传入,接下来,我们为x创建一个占位符,这将允许我们在稍后运行会话时传入一个数字。
x = tf.placeholder(tf.int64, name='x')
print(sess.run(2*x, feed_dict={x:3}))
sess.close()
6
1.1 - 线性函数
让我们通过计算以下等式来开始编程:Y=WX+b ,W和X是随机矩阵,b是随机向量。
我们计算WX+b,其中W,X和b是从随机正态分布中抽取的。 W的维度是(4,3),X是(3,1),b是(4,1)。 我们开始定义一个shape=(3,1)的常量X:
def linear_function():
"""
实现一个线性功能:
初始化W,类型为tensor的随机变量,维度为(4,3)
初始化X,类型为tensor的随机变量,维度为(3,1)
初始化b,类型为tensor的随机变量,维度为(4,1)
返回:
result - 运行了session后的结果,运行的是Y = WX + b
"""
np.random.seed(1)
X = np.random.randn(3,1)
W = np.random.randn(4,3)
b = np.random.randn(4,1)
# Y = tf.add(tf.matmul(W,X)+b)
Y = tf.matmul(W,X) + b
sess = tf.Session()
result = sess.run(Y)
sess.close() #session使用完毕,关闭它
return result
print('result = ' + str(linear_function()))
result = [[-2.15657382]
[ 2.95891446]
[-1.08926781]
[-0.84538042]]
1.2 - 计算sigmoid
def sigmoid(z):
x = tf.placeholder(tf.float32, name='x')
sigmoid = tf.sigmoid(x)
with tf.Session() as sess:
result = sess.run(sigmoid, feed_dict={x:z})
return result
print ("sigmoid(12) = " + str(sigmoid(12)))
print ("sigmoid(0) = " + str(sigmoid(0)))
sigmoid(12) = 0.999994
sigmoid(0) = 0.5
1.3 - 计算成本
1.4 - 使用独热编码(0、1编码)
独热编码 ------> one_hot_coding
很多时候在深度学习中y向量的维度是从0到C−1的,C是指分类的类别数量,如果C=4,那么对y而言你可能需要有以下的转换方式:
def one_hot_matrix(lables, C):
"""
创建一个矩阵,其中第i行对应第i个类号,第j列对应第j个训练样本
所以如果第j个样本对应着第i个标签,那么entry (i,j)将会是1
参数:
lables - 标签向量
C - 分类数
返回:
one_hot - 独热矩阵
"""
C = tf.constant(C, name='C')
one_hot_matrix = tf.one_hot(indices=lables, depth=C, axis=0)
# axis the direction of depth (0->row, 1->column)
sess = tf.Session()
one_hot = sess.run(one_hot_matrix)
sess.close()
return one_hot
lables = np.array([1, 2, 3, 0, 2, 1])
one_hot = one_hot_matrix(lables, 4)
print(str(one_hot))
print("------------------------------------")
lable2 = np.array([1,2,3,4,5,6,7,8,9])
two_hot = one_hot_matrix(lable2, 10)
print(str(two_hot))
[[ 0. 0. 0. 1. 0. 0.]
[ 1. 0. 0. 0. 0. 1.]
[ 0. 1. 0. 0. 1. 0.]
[ 0. 0. 1. 0. 0. 0.]]
------------------------------------
[[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 1. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 1. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 1. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 1.]]
1.5 - 初始化为0和1
现在我们将学习如何用0或者1初始化一个向量,我们要用到tf.ones()和tf.zeros(),给定这些函数一个维度值那么它们将会返回全是1或0的满足条件的向量/矩阵
def ones(shape):
ones = tf.ones(shape)
sess = tf.Session()
ones = sess.run(ones)
sess.close()
return ones
def zeros(shape):
ones = tf.zeros(shape)
sess = tf.Session()
ones = sess.run(ones)
sess.close()
return ones
print('ones = ' + str(ones([3,1])))
print('zeros= ' + str(zeros([4,1])))
ones = [[ 1.]
[ 1.]
[ 1.]]
zeros= [[ 0.]
[ 0.]
[ 0.]
[ 0.]]
2 - 使用TensorFlow构建你的第一个神经网络
X_train_orig , Y_train_orig , X_test_orig , Y_test_orig , classes = tf_utils.load_dataset()
index = 111
plt.imshow(X_train_orig[index])
print('Y = ' + str(np.squeeze(Y_train_orig[:, index])))
Y = 2
数字二
# X_train_orig.reshape(X_train_orig.shape[0], -1) # ? why is -1
# anwerser : (number, -1) this mean number is the cow, shape/number is the column
test_one = np.random.randn(4,5)
print(test_one.shape)
print(test_one.reshape(10, -1))
(4, 5)
[[ 0.58281521 -1.10061918]
[ 1.14472371 0.90159072]
[ 0.50249434 0.90085595]
[-0.68372786 -0.12289023]
[-0.93576943 -0.26788808]
[ 0.53035547 -0.69166075]
[-0.39675353 -0.6871727 ]
[-0.84520564 -0.67124613]
[-0.0126646 -1.11731035]
[ 0.2344157 1.65980218]]
和往常一样,我们要对数据集进行扁平化,然后再除以255以归一化数据,除此之外,我们要需要把每个标签转化为独热向量,像上面的图一样。
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T #每一列就是一个样本
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0],-1).T
print(X_train_flatten.shape)
print(X_train_orig.shape)
#归一化数据
X_train = X_train_flatten /255
X_test = X_test_flatten/255
#转换为独热矩阵
Y_train = tf_utils.convert_to_one_hot(Y_train_orig, 6)
Y_test = tf_utils.convert_to_one_hot(Y_test_orig, 6)
print("训练集样本数 = " + str(X_train.shape[1]))
print("测试集样本数 = " + str(X_test.shape[1]))
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))
(12288, 1080)
(1080, 64, 64, 3)
训练集样本数 = 1080
测试集样本数 = 120
X_train.shape: (12288, 1080)
Y_train.shape: (6, 1080)
X_test.shape: (12288, 120)
Y_test.shape: (6, 120)
我们的目标是构建能够高准确度识别符号的算法。 要做到这一点,你要建立一个TensorFlow模型,这个模型几乎和你之前在猫识别中使用的numpy一样(但现在使用softmax输出)。要将您的numpy实现与tensorflow实现进行比较的话这是一个很好的机会。
目前的模型是:LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX,SIGMOID输出层已经转换为SOFTMAX。当有两个以上的类时,一个SOFTMAX层将SIGMOID一般化。
2.1 - 创建placeholders
def create_placeholders(n_x, n_y):
"""
为TensorFlow会话创建占位符
参数:
n_x - 一个实数,图片向量的大小(64*64*3 = 12288)
n_y - 一个实数,分类数(从0到5,所以n_y = 6)
返回:
X - 一个数据输入的占位符,维度为[n_x, None],dtype = "float"
Y - 一个对应输入的标签的占位符,维度为[n_Y,None],dtype = "float"
提示:
使用None,因为它让我们可以灵活处理占位符提供的样本数量。事实上,测试/训练期间的样本数量是不同的。
"""
X = tf.placeholder(tf.float32, [n_x, None], name='X')
Y = tf.placeholder(tf.float32, [n_y, None], name='Y')
return X, Y
X, Y = create_placeholders(12288,6)
print('X = ' + str(X))
print('Y = ' + str(Y))
X = Tensor("X_2:0", shape=(12288, ?), dtype=float32)
Y = Tensor("Y_2:0", shape=(6, ?), dtype=float32)
2.2 - 初始化参数
初始化tensorflow中的参数,我们将使用Xavier初始化权重和用零来初始化偏差
def initialize_parameters():
tf.set_random_seed(1)
W1 = tf.get_variable('W1', [25, 12288], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b1 = tf.get_variable("b1",[25,1],initializer=tf.zeros_initializer())
W2 = tf.get_variable("W2", [12, 25], initializer = tf.contrib.layers.xavier_initializer(seed=1))
b2 = tf.get_variable("b2", [12, 1], initializer = tf.zeros_initializer())
W3 = tf.get_variable("W3", [6, 12], initializer = tf.contrib.layers.xavier_initializer(seed=1))
b3 = tf.get_variable("b3", [6, 1], initializer = tf.zeros_initializer())
parameters = {
'W1': W1,
'b1': b1,
'W2': W2,
'b2': b2,
'W3': W3,
'b3': b3
}
return parameters
tf.reset_default_graph() #用于清除默认图形堆栈并重置全局默认图形。
with tf.Session() as sess:
parameters = initialize_parameters()
print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))
W1 = <tf.Variable 'W1:0' shape=(25, 12288) dtype=float32_ref>
b1 = <tf.Variable 'b1:0' shape=(25, 1) dtype=float32_ref>
W2 = <tf.Variable 'W2:0' shape=(12, 25) dtype=float32_ref>
b2 = <tf.Variable 'b2:0' shape=(12, 1) dtype=float32_ref>
2.3 - 前向传播
我们将要在TensorFlow中实现前向传播,该函数将接受一个字典参数并完成前向传播,它会用到以下代码:
1. tf.add(…) :加法
2. tf.matmul(… , …) :矩阵乘法
3. tf.nn.relu(…) :Relu激活函数
def forward_propagation(X, parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']
Z1 = tf.add(tf.matmul(W1, X), b1)
A1 = tf.nn.relu(Z1)
Z2 = tf.add(tf.matmul(W2, A1), b2)
A2 = tf.nn.relu(Z2)
Z3 = tf.add(tf.matmul(W3, A2), b3)
return Z3
tf.reset_default_graph()
with tf.Session() as sess:
X,Y = create_placeholders(12288, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
print('Z3 = ' + str(Z3))
Z3 = Tensor("Add_2:0", shape=(6, ?), dtype=float32)
2.4 - 计算成本
def compute_cost(Z3,Y):
logits = tf.transpose(Z3) #转置
labels = tf.transpose(Y)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels))
return cost
tf.reset_default_graph()
with tf.Session() as sess:
X,Y = create_placeholders(12288,6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3,Y)
print('cost =' +str(cost))
cost =Tensor("Mean:0", shape=(), dtype=float32)
2.5 - 反向传播&更新参数
得益于编程框架,所有反向传播和参数更新都在1行代码中处理。计算成本函数后,将创建一个“optimizer”对象。 运行tf.session时,必须将此对象与成本函数一起调用,当被调用时,它将使用所选择的方法和学习速率对给定成本进行优化。
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost)
(n_x, m) = X_train.shape
print(n_x)
print(m)
12288
1080
2.6 - 构建模型
def model(X_train, Y_train, X_test, Y_test, learning_rate=0.0001,
num_epochs=1500,minibatch_size=32,print_cost=True, is_plot=True):
"""
实现一个三层的TensorFlow神经网络:LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX
参数:
X_train - 训练集,维度为(输入大小(输入节点数量) = 12288, 样本数量 = 1080)
Y_train - 训练集分类数量,维度为(输出大小(输出节点数量) = 6, 样本数量 = 1080)
X_test - 测试集,维度为(输入大小(输入节点数量) = 12288, 样本数量 = 120)
Y_test - 测试集分类数量,维度为(输出大小(输出节点数量) = 6, 样本数量 = 120)
learning_rate - 学习速率
num_epochs - 整个训练集的遍历次数
mini_batch_size - 每个小批量数据集的大小
print_cost - 是否打印成本,每100代打印一次
is_plot - 是否绘制曲线图
返回:
parameters - 学习后的参数
"""
ops.reset_default_graph() #能够重新运行模型而不覆盖tf变量
tf.set_random_seed(1)
seed = 3
(n_x, m) = X_train.shape #获取输入节点数量和样本数
n_y = Y_train.shape[0]
costs = [] #成本集
#给X和Y创建placeholder
X,Y = create_placeholders(n_x, n_y)
#初始化参数
parameters = initialize_parameters()
#前向传播
Z3 = forward_propagation(X, parameters)
#计算成本
cost = compute_cost(Z3,Y)
#反向传播,使用Adam优化
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
#初始化所有的变量
init = tf.global_variables_initializer()
#开始会话并计算
with tf.Session() as sess:
#初始化
sess.run(init)
#正常训练的循环
for epoch in range(num_epochs):
epoch_cost = 0 #每代的成本
num_minibatches = int(m / minibatch_size) #minibatch的总数量
seed = seed +1
minibatches = tf_utils.random_mini_batches(X_train, Y_train)
for minibatch in minibatches:
#选择一个minibatch
(minibatch_X, minibatch_Y) = minibatch
#数据已经准备好了,开始运行session
_, minibatch_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X,Y:minibatch_Y})
#计算这个minibatch在这一代中所占的误差
epoch_cost = epoch_cost + minibatch_cost / num_minibatches
#记录并打印成本
## 记录成本
if epoch % 5 == 0:
costs.append(epoch_cost)
if print_cost and epoch % 100 ==0:
print("epoch = " + str(epoch) + " epoch_cost = " + str(epoch_cost))
#是否绘制图谱
if is_plot:
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
parameters = sess.run(parameters)
print('参数已经保存到session。')
#计算当前的预测结果
correct_prediction = tf.equal(tf.argmax(Z3),tf.argmax(Y))
#计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
print("训练集的准确率:", accuracy.eval({X: X_train, Y: Y_train}))
print("测试集的准确率:", accuracy.eval({X: X_test, Y: Y_test}))
return parameters
2.7 - 运行的全过程
X_train_orig , Y_train_orig , X_test_orig , Y_test_orig , classes = tf_utils.load_dataset()
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T #每一列就是一个样本
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0],-1).T
print(X_train_flatten.shape)
print(X_train_orig.shape)
#归一化数据
X_train = X_train_flatten /255
X_test = X_test_flatten/255
#转换为独热矩阵
Y_train = tf_utils.convert_to_one_hot(Y_train_orig, 6)
Y_test = tf_utils.convert_to_one_hot(Y_test_orig, 6)
(12288, 1080)
(1080, 64, 64, 3)
def create_placeholders(n_x, n_y):
"""
为TensorFlow会话创建占位符
参数:
n_x - 一个实数,图片向量的大小(64*64*3 = 12288)
n_y - 一个实数,分类数(从0到5,所以n_y = 6)
返回:
X - 一个数据输入的占位符,维度为[n_x, None],dtype = "float"
Y - 一个对应输入的标签的占位符,维度为[n_Y,None],dtype = "float"
提示:
使用None,因为它让我们可以灵活处理占位符提供的样本数量。事实上,测试/训练期间的样本数量是不同的。
"""
X = tf.placeholder(tf.float32, [n_x, None], name='X')
Y = tf.placeholder(tf.float32, [n_y, None], name='Y')
return X, Y
def initialize_parameters():
tf.set_random_seed(1)
W1 = tf.get_variable('W1', [25, 12288], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b1 = tf.get_variable("b1",[25,1],initializer=tf.zeros_initializer())
W2 = tf.get_variable("W2", [12, 25], initializer = tf.contrib.layers.xavier_initializer(seed=1))
b2 = tf.get_variable("b2", [12, 1], initializer = tf.zeros_initializer())
W3 = tf.get_variable("W3", [6, 12], initializer = tf.contrib.layers.xavier_initializer(seed=1))
b3 = tf.get_variable("b3", [6, 1], initializer = tf.zeros_initializer())
parameters = {
'W1': W1,
'b1': b1,
'W2': W2,
'b2': b2,
'W3': W3,
'b3': b3
}
return parameters
def forward_propagation(X, parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']
Z1 = tf.add(tf.matmul(W1, X), b1)
A1 = tf.nn.relu(Z1)
Z2 = tf.add(tf.matmul(W2, A1), b2)
A2 = tf.nn.relu(Z2)
Z3 = tf.add(tf.matmul(W3, A2), b3)
return Z3
def compute_cost(Z3,Y):
logits = tf.transpose(Z3) #转置
labels = tf.transpose(Y)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels))
return cost
def model(X_train,Y_train,X_test,Y_test,
learning_rate=0.0001,num_epochs=1500,minibatch_size=32,
print_cost=True,is_plot=True):
"""
实现一个三层的TensorFlow神经网络:LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX
参数:
X_train - 训练集,维度为(输入大小(输入节点数量) = 12288, 样本数量 = 1080)
Y_train - 训练集分类数量,维度为(输出大小(输出节点数量) = 6, 样本数量 = 1080)
X_test - 测试集,维度为(输入大小(输入节点数量) = 12288, 样本数量 = 120)
Y_test - 测试集分类数量,维度为(输出大小(输出节点数量) = 6, 样本数量 = 120)
learning_rate - 学习速率
num_epochs - 整个训练集的遍历次数
mini_batch_size - 每个小批量数据集的大小
print_cost - 是否打印成本,每100代打印一次
is_plot - 是否绘制曲线图
返回:
parameters - 学习后的参数
"""
ops.reset_default_graph() #能够重新运行模型而不覆盖tf变量
tf.set_random_seed(1)
seed = 3
(n_x , m) = X_train.shape #获取输入节点数量和样本数
n_y = Y_train.shape[0] #获取输出节点数量
costs = [] #成本集
#给X和Y创建placeholder
X,Y = create_placeholders(n_x,n_y)
#初始化参数
parameters = initialize_parameters()
#前向传播
Z3 = forward_propagation(X,parameters)
#计算成本
cost = compute_cost(Z3,Y)
#反向传播,使用Adam优化
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
#初始化所有的变量
init = tf.global_variables_initializer()
#开始会话并计算
with tf.Session() as sess:
#初始化
sess.run(init)
#正常训练的循环
for epoch in range(num_epochs):
epoch_cost = 0 #每代的成本
num_minibatches = int(m / minibatch_size) #minibatch的总数量
seed = seed + 1
minibatches = tf_utils.random_mini_batches(X_train,Y_train,minibatch_size,seed)
for minibatch in minibatches:
#选择一个minibatch
(minibatch_X,minibatch_Y) = minibatch
#数据已经准备好了,开始运行session
_ , minibatch_cost = sess.run([optimizer,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})
#计算这个minibatch在这一代中所占的误差
epoch_cost = epoch_cost + minibatch_cost / num_minibatches
#记录并打印成本
## 记录成本
if epoch % 5 == 0:
costs.append(epoch_cost)
#是否打印:
if print_cost and epoch % 100 == 0:
print("epoch = " + str(epoch) + " epoch_cost = " + str(epoch_cost))
#是否绘制图谱
if is_plot:
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
#保存学习后的参数
parameters = sess.run(parameters)
print("参数已经保存到session。")
#计算当前的预测结果
correct_prediction = tf.equal(tf.argmax(Z3),tf.argmax(Y))
#计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
print("训练集的准确率:", accuracy.eval({X: X_train, Y: Y_train}))
print("测试集的准确率:", accuracy.eval({X: X_test, Y: Y_test}))
return parameters
start_time = time.clock()
parameters = model(X_train, Y_train, X_test, Y_test)
end_time = time.clock()
print("CPU的执行时间 = " + str(end_time - start_time) + " 秒" )
epoch = 0 epoch_cost = 1.85570190892
epoch = 100 epoch_cost = 1.01645778345
epoch = 200 epoch_cost = 0.733102415547
epoch = 300 epoch_cost = 0.572939646967
epoch = 400 epoch_cost = 0.468774231997
epoch = 500 epoch_cost = 0.381020727031
epoch = 600 epoch_cost = 0.313821615143
epoch = 700 epoch_cost = 0.254157840302
epoch = 800 epoch_cost = 0.203829386921
epoch = 900 epoch_cost = 0.166421434644
epoch = 1000 epoch_cost = 0.141485600083
epoch = 1100 epoch_cost = 0.107580181776
epoch = 1200 epoch_cost = 0.0862698159886
epoch = 1300 epoch_cost = 0.0593705453317
epoch = 1400 epoch_cost = 0.0522282078975
仿真图
参数已经保存到session。
训练集的准确率: 0.999074
测试集的准确率: 0.716667
CPU的执行时间 = 1185.987672 秒
3 - 预测
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
my_image1 = '5.png'
fileName1 = 'datasets/fingers/' + my_image1
image1 = mpimg.imread(fileName1)
plt.imshow(image1)
my_image1 = image1.reshape(1, 64*64*3).T
my_image_prediction = tf_utils.predict(my_image1, parameters)
print('预测结果: y = ' + str(np.squeeze(my_image_prediction)))
预测结果: y = 5
数字五
my_image1 = '4.png'
fileName1 = 'datasets/fingers/' + my_image1
image1 = mpimg.imread(fileName1)
plt.imshow(image1)
my_image1 = image1.reshape(1, 64*64*3).T
my_image_prediction = tf_utils.predict(my_image1, parameters)
print('预测结果: y = ' + str(np.squeeze(my_image_prediction)))
预测结果: y = 2
数字四
看样子还要在继续改进!
my_image1 = '3.png'
fileName1 = 'datasets/fingers/' + my_image1
image1 = mpimg.imread(fileName1)
plt.imshow(image1)
my_image1 = image1.reshape(1, 64*64*3).T
my_image_prediction = tf_utils.predict(my_image1, parameters)
print('预测结果: y = ' + str(np.squeeze(my_image_prediction)))
预测结果: y = 2
数字三
my_image1 = '2.png'
fileName1 = 'datasets/fingers/' + my_image1
image1 = mpimg.imread(fileName1)
plt.imshow(image1)
my_image1 = image1.reshape(1, 64*64*3).T
my_image_prediction = tf_utils.predict(my_image1, parameters)
print('预测结果: y = ' + str(np.squeeze(my_image_prediction)))
预测结果: y = 1
数字二
my_image1 = '1.png'
fileName1 = 'datasets/fingers/' + my_image1
image1 = mpimg.imread(fileName1)
plt.imshow(image1)
my_image1 = image1.reshape(1, 64*64*3).T
my_image_prediction = tf_utils.predict(my_image1, parameters)
print('预测结果: y = ' + str(np.squeeze(my_image_prediction)))
预测结果: y = 1