网站怎么做百度优化,高端网站建设公司名字,wordpress制作客户端,骑行网站模板文章目录作业1#xff1a;实现卷积神经网络1. 导入一些包2. 模型框架3. 卷积神经网络3.1 Zero-Padding3.2 单步卷积3.3 卷积神经网络 - 前向传播4. 池化层5. 卷积神经网络 - 反向传播5.1 卷积层反向传播5.1.1 计算 dA5.1.2 计算 dW5.1.3 计算 db5.2 池化层 - 反向传播5.2.1 最…
文章目录作业1实现卷积神经网络1. 导入一些包2. 模型框架3. 卷积神经网络3.1 Zero-Padding3.2 单步卷积3.3 卷积神经网络 - 前向传播4. 池化层5. 卷积神经网络 - 反向传播5.1 卷积层反向传播5.1.1 计算 dA5.1.2 计算 dW5.1.3 计算 db5.2 池化层 - 反向传播5.2.1 最大池化 - 反向传播5.2.2 平均池化 - 反向传播5.2.3 组合在一起 - 反向池化作业2用TensorFlow实现卷积神经网络1. TensorFlow 模型1.1 创建 placeholder1.2 初始化参数1.3 前向传播1.4 计算损失1.5 模型测试题参考博文
笔记04.卷积神经网络 W1.卷积神经网络
作业1实现卷积神经网络
1. 导入一些包
import numpy as np
import h5py
import matplotlib.pyplot as plt%matplotlib inline
plt.rcParams[figure.figsize] (5.0, 4.0) # set default size of plots
plt.rcParams[image.interpolation] nearest
plt.rcParams[image.cmap] gray%load_ext autoreload
%autoreload 2np.random.seed(1)2. 模型框架 3. 卷积神经网络 卷积神经网络会将输入转化为一个维度大小不一样的输出
3.1 Zero-Padding
0 padding 会在图片周围填充 0 元素下图 p2p2p2 padding 的好处
减少深层网络里图片尺寸衰减问题保留更多的图片边缘的信息
# 给第2、4个维度 padding 1层3层像素
a np.pad(a, ((0,0), (1,1), (0,0), (3,3), (0,0)), constant, constant_values (..,..))# GRADED FUNCTION: zero_paddef zero_pad(X, pad):Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image, as illustrated in Figure 1.Argument:X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m imagespad -- integer, amount of padding around each image on vertical and horizontal dimensionsReturns:X_pad -- padded image of shape (m, n_H 2*pad, n_W 2*pad, n_C)### START CODE HERE ### (≈ 1 line)X_pad np.pad(X, ((0,0), # 样本(pad,pad), # 高(pad,pad), # 宽(0,0)),# 通道constant, constant_values(0))### END CODE HERE ###return X_pad3.2 单步卷积 # GRADED FUNCTION: conv_single_stepdef conv_single_step(a_slice_prev, W, b):Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation of the previous layer.Arguments:a_slice_prev -- slice of input data of shape (f, f, n_C_prev)W -- Weight parameters contained in a window - matrix of shape (f, f, n_C_prev)b -- Bias parameters contained in a window - matrix of shape (1, 1, 1)Returns:Z -- a scalar value, result of convolving the sliding window (W, b) on a slice x of the input data### START CODE HERE ### (≈ 2 lines of code)# Element-wise product between a_slice and W. Do not add the bias yet.s a_slice_prev*W# Sum over all entries of the volume s.Z np.sum(s)# Add bias b to Z. Cast b to a float() so that Z results in a scalar value.Z Z float(b)### END CODE HERE ###return Z3.3 卷积神经网络 - 前向传播 nH⌊nHprev−f2×padstride⌋1n_H \lfloor \frac{n_{H_{prev}} - f 2 \times pad}{stride} \rfloor 1 nH⌊stridenHprev−f2×pad⌋1 nW⌊nWprev−f2×padstride⌋1n_W \lfloor \frac{n_{W_{prev}} - f 2 \times pad}{stride} \rfloor 1 nW⌊stridenWprev−f2×pad⌋1 nC卷积过滤器数量n_C \text{卷积过滤器数量}nC卷积过滤器数量
# GRADED FUNCTION: conv_forwarddef conv_forward(A_prev, W, b, hparameters):Implements the forward propagation for a convolution functionArguments:A_prev -- output activations of the previous layer, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)b -- Biases, numpy array of shape (1, 1, 1, n_C)hparameters -- python dictionary containing stride and padReturns:Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)cache -- cache of values needed for the conv_backward() function### START CODE HERE #### Retrieve dimensions from A_prevs shape (≈1 line) (m, n_H_prev, n_W_prev, n_C_prev) A_prev.shape# Retrieve dimensions from Ws shape (≈1 line)(f, f, n_C_prev, n_C) W.shape# Retrieve information from hparameters (≈2 lines)stride hparameters[stride]pad hparameters[pad]# Compute the dimensions of the CONV output volume using the formula given above. # Hint: use int() to floor. (≈2 lines)n_H (n_H_prev-f2*pad)//stride 1n_W (n_W_prev-f2*pad)//stride 1# Initialize the output volume Z with zeros. (≈1 line)Z np.zeros((m, n_H, n_W, n_C))# Create A_prev_pad by padding A_prevA_prev_pad zero_pad(A_prev, pad)for i in range(m): # loop over the batch of training examplesa_prev_pad A_prev_pad[i, :] # Select ith training examples padded activationfor h in range(n_H): # loop over vertical axis of the output volumefor w in range(n_W): # loop over horizontal axis of the output volumefor c in range(n_C): # loop over channels ( #filters) of the output volume# Find the corners of the current slice (≈4 lines)vert_start h*stridevert_end vert_start fhoriz_start w*stridehoriz_end horiz_start f# Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)a_slice_prev a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]# Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈1 line)Z[i, h, w, c] np.sum(conv_single_step(a_slice_prev, W[:,:,:,c], b[:,:,:,c]))### END CODE HERE #### Making sure your output shape is correctassert(Z.shape (m, n_H, n_W, n_C))# Save information in cache for the backpropcache (A_prev, W, b, hparameters)return Z, cache4. 池化层
池化层不改变通道数量下面公式没有padding p 0 nH⌊nHprev−fstride⌋1n_H \lfloor \frac{n_{H_{prev}} - f}{stride} \rfloor 1 nH⌊stridenHprev−f⌋1 nW⌊nWprev−fstride⌋1n_W \lfloor \frac{n_{W_{prev}} - f}{stride} \rfloor 1 nW⌊stridenWprev−f⌋1 nCnCprevn_C n_{C_{prev}}nCnCprev
# GRADED FUNCTION: pool_forwarddef pool_forward(A_prev, hparameters, mode max):Implements the forward pass of the pooling layerArguments:A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)hparameters -- python dictionary containing f and stridemode -- the pooling mode you would like to use, defined as a string (max or average)Returns:A -- output of the pool layer, a numpy array of shape (m, n_H, n_W, n_C)cache -- cache used in the backward pass of the pooling layer, contains the input and hparameters # Retrieve dimensions from the input shape(m, n_H_prev, n_W_prev, n_C_prev) A_prev.shape# Retrieve hyperparameters from hparametersf hparameters[f]stride hparameters[stride]# Define the dimensions of the outputn_H 1 (n_H_prev - f) // striden_W 1 (n_W_prev - f) // striden_C n_C_prev# Initialize output matrix AA np.zeros((m, n_H, n_W, n_C)) ### START CODE HERE ###for i in range(m): # loop over the training examplesfor h in range(n_H): # loop on the vertical axis of the output volumefor w in range(n_W): # loop on the horizontal axis of the output volumefor c in range (n_C): # loop over the channels of the output volume# Find the corners of the current slice (≈4 lines)vert_start h*stridevert_end vert_start fhoriz_start w*stridehoriz_end horiz_start f# Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)a_prev_slice A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]# Compute the pooling operation on the slice. Use an if statment to differentiate the modes. Use np.max/np.mean.if mode max:A[i, h, w, c] np.max(a_prev_slice)elif mode average:A[i, h, w, c] np.mean(a_prev_slice)### END CODE HERE #### Store the input and hparameters in cache for pool_backward()cache (A_prev, hparameters)# Making sure your output shape is correctassert(A.shape (m, n_H, n_W, n_C))return A, cache5. 卷积神经网络 - 反向传播
现代机器学习框架一般都会自动帮你实现反向传播下面再过一遍
5.1 卷积层反向传播
5.1.1 计算 dA
dA∑h0nH∑w0nWWc×dZhwdA \sum _{h0} ^{n_H} \sum_{w0} ^{n_W} W_c \times dZ_{hw}dAh0∑nHw0∑nWWc×dZhw
WcW_cWc 是一个过滤器dZhwdZ_{hw}dZhw 是输出的卷积层 ZZZ 的hw位置的梯度
代码
da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] W[:,:,:,c] * dZ[i, h, w, c]5.1.2 计算 dW
dWc∑h0nH∑w0nWaslice×dZhwdW_c \sum _{h0} ^{n_H} \sum_{w0} ^ {n_W} a_{slice} \times dZ_{hw}dWch0∑nHw0∑nWaslice×dZhw
代码
dW[:,:,:,c] a_slice * dZ[i, h, w, c]5.1.3 计算 db
db∑h∑wdZhwdb \sum_h \sum_w dZ_{hw}dbh∑w∑dZhw 代码
db[:,:,:,c] dZ[i, h, w, c]def conv_backward(dZ, cache):Implement the backward propagation for a convolution functionArguments:dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)cache -- cache of values needed for the conv_backward(), output of conv_forward()Returns:dA_prev -- gradient of the cost with respect to the input of the conv layer (A_prev),numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)dW -- gradient of the cost with respect to the weights of the conv layer (W)numpy array of shape (f, f, n_C_prev, n_C)db -- gradient of the cost with respect to the biases of the conv layer (b)numpy array of shape (1, 1, 1, n_C)### START CODE HERE #### Retrieve information from cache(A_prev, W, b, hparameters) cache# Retrieve dimensions from A_prevs shape(m, n_H_prev, n_W_prev, n_C_prev) A_prev.shape# Retrieve dimensions from Ws shape(f, f, n_C_prev, n_C) W.shape# Retrieve information from hparametersstride hparameters[stride]pad hparameters[pad]# Retrieve dimensions from dZs shape(m, n_H, n_W, n_C) dZ.shape# Initialize dA_prev, dW, db with the correct shapesdA_prev np.zeros(A_prev.shape) dW np.zeros(W.shape)db np.zeros((1, 1, 1, n_C))# Pad A_prev and dA_prev, 添加周围pad像素A_prev_pad zero_pad(A_prev, pad)dA_prev_pad zero_pad(dA_prev, pad)for i in range(m): # loop over the training examples# select ith training example from A_prev_pad and dA_prev_pada_prev_pad A_prev_pad[i]da_prev_pad dA_prev_pad[i]for h in range(n_H): # loop over vertical axis of the output volumefor w in range(n_W): # loop over horizontal axis of the output volumefor c in range(n_C): # loop over the channels of the output volume# Find the corners of the current slicevert_start h*stridevert_end vert_start fhoriz_start w*stridehoriz_end horiz_start f# Use the corners to define the slice from a_prev_pada_slice a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]# Update gradients for the window and the filters parameters using the code formulas given aboveda_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] W[:,:,:,c]*dZ[i,h,w,c]dW[:,:,:,c] a_slice*dZ[i,h,w,c]db[:,:,:,c] dZ[i,h,w,c]# Set the ith training examples dA_prev to the unpaded da_prev_pad (Hint: use X[pad:-pad, pad:-pad, :])dA_prev[i, :, :, :] da_prev_pad[pad:-pad, pad:-pad, :]### END CODE HERE #### Making sure your output shape is correctassert(dA_prev.shape (m, n_H_prev, n_W_prev, n_C_prev))return dA_prev, dW, db5.2 池化层 - 反向传播
池化层没有参数需要更新
5.2.1 最大池化 - 反向传播
先建立一个辅助函数create_mask_from_window() X[1342]→M[0010]X \begin{bmatrix} 1 3 \\ 4 2 \end{bmatrix} \quad \rightarrow \quad M \begin{bmatrix} 0 0 \\ 1 0 \end{bmatrix}X[1432]→M[0100] 可以使用 x np.max(X), A (X x)
def create_mask_from_window(x):Creates a mask from an input matrix x, to identify the max entry of x.Arguments:x -- Array of shape (f, f)Returns:mask -- Array of the same shape as window, contains a True at the position corresponding to the max entry of x.### START CODE HERE ### (≈1 line)mask (x np.max(x))### END CODE HERE ###return mask5.2.2 平均池化 - 反向传播
平均池化输入的每个元素是一样的重要对于输出反向传播时 dZ1→dZ[1/41/41/41/4]dZ 1 \quad \rightarrow \quad dZ \begin{bmatrix} 1/4 1/4 \\ 1/4 1/4 \end{bmatrix}dZ1→dZ[1/41/41/41/4]
def distribute_value(dz, shape):Distributes the input value in the matrix of dimension shapeArguments:dz -- input scalarshape -- the shape (n_H, n_W) of the output matrix for which we want to distribute the value of dzReturns:a -- Array of size (n_H, n_W) for which we distributed the value of dz### START CODE HERE #### Retrieve dimensions from shape (≈1 line)(n_H, n_W) shape# Compute the value to distribute on the matrix (≈1 line)average dz/(n_H*n_W)# Create a matrix where every entry is the average value (≈1 line)a np.ones((n_H, n_W))*average### END CODE HERE ###return a5.2.3 组合在一起 - 反向池化
def pool_backward(dA, cache, mode max):Implements the backward pass of the pooling layerArguments:dA -- gradient of cost with respect to the output of the pooling layer, same shape as Acache -- cache output from the forward pass of the pooling layer, contains the layers input and hparameters mode -- the pooling mode you would like to use, defined as a string (max or average)Returns:dA_prev -- gradient of cost with respect to the input of the pooling layer, same shape as A_prev### START CODE HERE #### Retrieve information from cache (≈1 line)(A_prev, hparameters) cache# Retrieve hyperparameters from hparameters (≈2 lines)stride hparameters[stride]f hparameters[f]# Retrieve dimensions from A_prevs shape and dAs shape (≈2 lines)m, n_H_prev, n_W_prev, n_C_prev A_prev.shapem, n_H, n_W, n_C dA.shape# Initialize dA_prev with zeros (≈1 line)dA_prev np.zeros(A_prev.shape)for i in range(m): # loop over the training examples# select training example from A_prev (≈1 line)a_prev A_prev[i]for h in range(n_H): # loop on the vertical axisfor w in range(n_W): # loop on the horizontal axisfor c in range(n_C): # loop over the channels (depth)# Find the corners of the current slice (≈4 lines)vert_start h*stridevert_end vert_start fhoriz_start w*stridehoriz_end horiz_start f# Compute the backward propagation in both modes.if mode max:# Use the corners and c to define the current slice from a_prev (≈1 line)a_prev_slice a_prev[vert_start:vert_end, horiz_start:horiz_end, c]# Create the mask from a_prev_slice (≈1 line)mask create_mask_from_window(a_prev_slice)# Set dA_prev to be dA_prev (the mask multiplied by the correct entry of dA) (≈1 line)dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] mask*dA[i, h, w, c]elif mode average:# Get the value a from dA (≈1 line)da dA[i, vert_start, horiz_start, c]# Define the shape of the filter as fxf (≈1 line)shape (f, f)# Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] distribute_value(da, shape)### END CODE #### Making sure your output shape is correctassert(dA_prev.shape A_prev.shape)return dA_prev作业2用TensorFlow实现卷积神经网络
上次作业02.改善深层神经网络超参数调试、正则化以及优化 W3. 超参数调试、Batch Norm和程序框架作业TensorFlow教程数字手势预测
1. TensorFlow 模型
导入一些包
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)import sys
sys.path.append(/path/file)# Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes load_dataset()手势数字数据集
查看图片
# Example of a picture
index 7
plt.imshow(X_train_orig[index])
print (y str(np.squeeze(Y_train_orig[:, index])))y 1了解数据维度
X_train X_train_orig/255. # 归一化
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 {}输出
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)1.1 创建 placeholder
placeholder 给输入数据创建一个位子后面给他 feed 数据样本数量维度可以置为 None
# GRADED FUNCTION: create_placeholdersdef create_placeholders(n_H0, n_W0, n_C0, n_y):Creates the placeholders for the tensorflow session.Arguments:n_H0 -- scalar, height of an input imagen_W0 -- scalar, width of an input imagen_C0 -- scalar, number of channels of the inputn_y -- scalar, number of classesReturns:X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype floatY -- placeholder for the input labels, of shape [None, n_y] and dtype float### START CODE HERE ### (≈2 lines)X tf.placeholder(tf.float32, shape(None, n_H0, n_W0, n_C0), nameX)Y tf.placeholder(tf.float32, shape(None, n_y), nameY)### END CODE HERE ###return X, Y1.2 初始化参数
初始化权重/过滤器 W1,W2W_1, W_2W1,W2 tf.contrib.layers.xavier_initializer(seed 0) TensorFlow 会处理偏置你无需担心还会自动初始化全连接层
W tf.get_variable(W, [1,2,3,4], initializer ...)# GRADED FUNCTION: initialize_parametersdef initialize_parameters():Initializes weight parameters to build a neural network with tensorflow. The shapes are:W1 : [4, 4, 3, 8]W2 : [2, 2, 8, 16]Returns:parameters -- a dictionary of tensors containing W1, W2tf.set_random_seed(1) # so that your random numbers match ours### START CODE HERE ### (approx. 2 lines of code)W1 tf.get_variable(nameW1, shape[4,4,3,8], dtypetf.float32, initializertf.contrib.layers.xavier_initializer(seed0))W2 tf.get_variable(nameW2, shape[2,2,8,16], dtypetf.float32, initializertf.contrib.layers.xavier_initializer(seed0))### END CODE HERE ###parameters {W1: W1,W2: W2}return parameterstf.reset_default_graph()
with tf.Session() as sess_test:parameters initialize_parameters()init tf.global_variables_initializer()sess_test.run(init)print(W1 str(parameters[W1].eval()[1,1,1]))print(W2 str(parameters[W2].eval()[1,1,1]))输出
W1 [ 0.00131723 0.1417614 -0.04434952 0.09197326 0.14984085 -0.03514394-0.06847463 0.05245192]
W2 [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058-0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228-0.22779644 -0.1601823 -0.16117483 -0.10286498]1.3 前向传播
tf.nn.conv2d(X,W1, strides [1,s,s,1], padding SAME)strides是各维度的步长参考TF文档tf.nn.max_pool(A, ksize [1,f,f,1], strides [1,s,s,1], padding SAME)ksize是窗口大小 fxf参考TF文档tf.nn.relu(Z1)激活函数tf.contrib.layers.flatten(P)将输入 P展平成一维向量 shape [batch_size, k]参考TF文档tf.contrib.layers.fully_connected(F, num_outputs)给一个展平的 F 输入返回用全连接层计算后的输出参考TF文档注当训练模型时该模块会自动初始化权重并训练你无需初始化它
模型架构CONV2D - RELU - MAXPOOL - CONV2D - RELU - MAXPOOL - FLATTEN - FULLYCONNECTED
参数如下 Conv2D: stride 1, padding is “SAME”ReLUMax pool: Use an 8 by 8 filter size and an 8 by 8 stride, padding is “SAME”Conv2D: stride 1, padding is “SAME”ReLUMax pool: Use a 4 by 4 filter size and a 4 by 4 stride, padding is “SAME”Flatten the previous output.FULLYCONNECTED (FC) layer: Apply a fully connected layer without an non-linear activation function. 不要计算激活输出TF有模块会一起计算 激活和cost # GRADED FUNCTION: forward_propagationdef forward_propagation(X, parameters):Implements the forward propagation for the model:CONV2D - RELU - MAXPOOL - CONV2D - RELU - MAXPOOL - FLATTEN - FULLYCONNECTEDArguments:X -- input dataset placeholder, of shape (input size, number of examples)parameters -- python dictionary containing your parameters W1, W2the shapes are given in initialize_parametersReturns:Z3 -- the output of the last LINEAR unit# Retrieve the parameters from the dictionary parameters W1 parameters[W1]W2 parameters[W2]### START CODE HERE #### CONV2D: stride of 1, padding SAMEZ1 tf.nn.conv2d(X, W1, strides[1,1,1,1], paddingSAME)# RELUA1 tf.nn.relu(Z1)# MAXPOOL: window 8x8, sride 8, padding SAMEP1 tf.nn.max_pool(A1, ksize[1,8,8,1],strides[1,8,8,1],paddingSAME)# CONV2D: filters W2, stride 1, padding SAMEZ2 tf.nn.conv2d(P1, W2, strides[1,1,1,1],paddingSAME)# RELUA2 tf.nn.relu(Z2)# MAXPOOL: window 4x4, stride 4, padding SAMEP2 tf.nn.max_pool(A2, ksize[1,4,4,1],strides[1,4,4,1],paddingSAME)# FLATTENP2 tf.contrib.layers.flatten(P2)# FULLY-CONNECTED without non-linear activation function (not not call softmax).# 6 neurons in output layer. Hint: one of the arguments should be activation_fnNone Z3 tf.contrib.layers.fully_connected(P2, num_outputs6, activation_fnNone)### END CODE HERE ###return Z3tf.reset_default_graph()with tf.Session() as sess:np.random.seed(1)X, Y create_placeholders(64, 64, 3, 6)parameters initialize_parameters()Z3 forward_propagation(X, parameters)init tf.global_variables_initializer()sess.run(init)a sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)})print(Z3 str(a))输出可能是版本原因跟标准答案不一样
Z3 [[ 1.4416984 -0.24909666 5.450499 -0.2618962 -0.20669907 1.3654671 ][ 1.4070846 -0.02573211 5.08928 -0.48669922 -0.40940708 1.2624859 ]]1.4 计算损失
tf.nn.softmax_cross_entropy_with_logits(logits Z3, labels Y)计算softmax激活 及 熵损失tf.reduce_mean计算均值
# GRADED FUNCTION: compute_cost def compute_cost(Z3, Y):Computes the costArguments:Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)Y -- true labels vector placeholder, same shape as Z3Returns:cost - Tensor of the cost function### START CODE HERE ### (1 line of code)cost tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logitsZ3, labelsY))### END CODE HERE ###return costtf.reset_default_graph()with tf.Session() as sess:np.random.seed(1)X, Y create_placeholders(64, 64, 3, 6)parameters initialize_parameters()Z3 forward_propagation(X, parameters)cost compute_cost(Z3, Y)init tf.global_variables_initializer()sess.run(init)a sess.run(cost, {X: np.random.randn(4,64,64,3), Y: np.random.randn(4,6)})print(cost str(a))输出
cost 4.6648693 # 跟标准答案不一样1.5 模型
创建 placeholders初始化参数前向传播计算损失创建优化器
# GRADED FUNCTION: modeldef model(X_train, Y_train, X_test, Y_test, learning_rate 0.009,num_epochs 100, minibatch_size 64, print_cost True):Implements a three-layer ConvNet in Tensorflow:CONV2D - RELU - MAXPOOL - CONV2D - RELU - MAXPOOL - FLATTEN - FULLYCONNECTEDArguments:X_train -- training set, of shape (None, 64, 64, 3)Y_train -- test set, of shape (None, n_y 6)X_test -- training set, of shape (None, 64, 64, 3)Y_test -- test set, of shape (None, n_y 6)learning_rate -- learning rate of the optimizationnum_epochs -- number of epochs of the optimization loopminibatch_size -- size of a minibatchprint_cost -- True to print the cost every 100 epochsReturns:train_accuracy -- real number, accuracy on the train set (X_train)test_accuracy -- real number, testing accuracy on the test set (X_test)parameters -- parameters learnt by the model. They can then be used to predict.ops.reset_default_graph() # to be able to rerun the model without overwriting tf variablestf.set_random_seed(1) # to keep results consistent (tensorflow seed)seed 3 # to keep results consistent (numpy seed)(m, n_H0, n_W0, n_C0) X_train.shape n_y Y_train.shape[1] costs [] # To keep track of the cost# Create Placeholders of the correct shape### START CODE HERE ### (1 line)X, Y create_placeholders(n_H0, n_W0, n_C0, n_y)### END CODE HERE #### Initialize parameters### START CODE HERE ### (1 line)parameters initialize_parameters()### END CODE HERE #### Forward propagation: Build the forward propagation in the tensorflow graph### START CODE HERE ### (1 line)Z3 forward_propagation(X, parameters)### END CODE HERE #### Cost function: Add cost function to tensorflow graph### START CODE HERE ### (1 line)cost compute_cost(Z3, Y)### END CODE HERE #### Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.### START CODE HERE ### (1 line)optimizer tf.train.AdamOptimizer(learning_ratelearning_rate).minimize(cost)### END CODE HERE #### Initialize all the variables globallyinit tf.global_variables_initializer()# Start the session to compute the tensorflow graphwith tf.Session() as sess:# Run the initializationsess.run(init)# Do the training loopfor epoch in range(num_epochs):minibatch_cost 0.num_minibatches m // minibatch_size# number of minibatches of size minibatch_size in the train setseed seed 1minibatches random_mini_batches(X_train, Y_train, minibatch_size, seed)for minibatch in minibatches:# Select a minibatch(minibatch_X, minibatch_Y) minibatch# IMPORTANT: The line that runs the graph on a minibatch.# Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).### START CODE HERE ### (1 line)_ , temp_cost sess.run([optimizer, cost], feed_dict{X:minibatch_X, Y:minibatch_Y})### END CODE HERE ###minibatch_cost temp_cost / num_minibatches# Print the cost every epochif print_cost True and epoch % 5 0:print (Cost after epoch %i: %f % (epoch, minibatch_cost))if print_cost True and epoch % 1 0:costs.append(minibatch_cost)# plot the costplt.plot(np.squeeze(costs))plt.ylabel(cost)plt.xlabel(iterations (per tens))plt.title(Learning rate str(learning_rate))plt.show()# Calculate the correct predictionspredict_op tf.argmax(Z3, 1)correct_prediction tf.equal(predict_op, tf.argmax(Y, 1))# Calculate accuracy on the test setaccuracy tf.reduce_mean(tf.cast(correct_prediction, float))print(accuracy)train_accuracy accuracy.eval({X: X_train, Y: Y_train})test_accuracy accuracy.eval({X: X_test, Y: Y_test})print(Train Accuracy:, train_accuracy)print(Test Accuracy:, test_accuracy)return train_accuracy, test_accuracy, parameters训练模型 100 epochs
_, _, parameters model(X_train, Y_train, X_test, Y_test)使用作业默认的学习率0.009和迭代次数100效果很差只有60%50%多的训练准确率和测试准确率
更改为learning_rate 0.002, num_epochs 600
Cost after epoch 0: 1.943088
Cost after epoch 5: 1.885871
Cost after epoch 10: 1.824765
Cost after epoch 15: 1.595936
Cost after epoch 20: 1.243416
Cost after epoch 25: 1.004351
Cost after epoch 30: 0.875302
Cost after epoch 35: 0.767196
Cost after epoch 40: 0.711865
Cost after epoch 45: 0.640964
Cost after epoch 50: 0.574520
。。。。
Cost after epoch 150: 0.207593
。。。。
Cost after epoch 300: 0.071819
。。。。
Cost after epoch 500: 0.013352
。。。。
Cost after epoch 595: 0.016594Tensor(Mean_1:0, shape(), dtypefloat32)
Train Accuracy: 0.9916667
Test Accuracy: 0.9训练集上准确率为 99%测试集上准确率为 90%存在一定的过拟合。
fname images/thumbs_up.jpg
image np.array(ndimage.imread(fname, flattenFalse))
my_image scipy.misc.imresize(image, size(64,64))
plt.imshow(my_image)一起加油学习冲啊 我的CSDN博客地址 https://michael.blog.csdn.net/
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