From f1c0a2eec4698faa21c160971ea6460539e1110a Mon Sep 17 00:00:00 2001 From: p5tm2eri3 Date: Mon, 25 Apr 2022 18:03:31 +0800 Subject: [PATCH 1/6] Update README.md --- README.md | 122 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) diff --git a/README.md b/README.md index 0857c95..5cfe3b4 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,124 @@ # pattern_recognition +from __future__ import division, print_function, absolute_import + +# Import MNIST data，MNIST数据集导入 +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("MNIST_data", one_hot=False) + +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np + +# Training Parameters，超参数 +learning_rate = 0.001 #学习率 +num_steps = 2000 # 训练步数 +batch_size = 128 # 训练数据批的大小 + +# Network Parameters，网络参数 +num_input = 784 # MNIST数据输入 (img shape: 28*28) +num_classes = 10 # MNIST所有类别 (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25 + +# Create the neural network，创建深度神经网络 +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + + # Define a scope for reusing the variables，确定命名空间 + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator类型的输入为像素 + x = x_dict['images'] + + # MNIST数据输入格式为一位向量，包含784个特征 (28*28像素) + # 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数] + # 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # 卷积层，32个卷积核，尺寸为5x5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # 最大池化层，步长为2，无需学习任何参量 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # 卷积层，64个卷积核，尺寸为3x3 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # 最大池化层，步长为2，无需学习任何参量 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # 展开特征为一维向量，以输入全连接层 + fc1 = tf.contrib.layers.flatten(conv2) + + # 全连接层 + fc1 = tf.layers.dense(fc1, 1024) + # 应用Dropout (训练时打开，测试时关闭) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # 输出层，预测类别 + out = tf.layers.dense(fc1, n_classes) + + return out + +# 确定模型功能 (参照TF Estimator模版) +def model_fn(features, labels, mode): + + # 构建神经网络 + # 因为dropout在训练与测试时的特性不一，我们此处为训练和测试过程创建两个独立但共享权值的计算图 + logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True) + logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False) + + # 预测 + pred_classes = tf.argmax(logits_test, axis=1) + pred_probas = tf.nn.softmax(logits_test) + + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # 确定误差函数与优化器 + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) + + # 评估模型精确度 + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators需要返回EstimatorSpec + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# 构建Estimator +model = tf.estimator.Estimator(model_fn) + +# 确定训练输入函数 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# 开始训练模型 +model.train(input_fn, steps=num_steps) + +# 评判模型 +# 确定评判用输入函数 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +model.evaluate(input_fn) + +# 预测单个图像 +n_images = 6 +# 从数据集得到测试图像 +test_images = mnist.test.images[:n_images] +# 准备输入数据 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': test_images}, shuffle=False) +# 用训练好的模型预测图片类别 +preds = list(model.predict(input_fn)) + +# 可视化显示 +for i in range(n_images): + plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') + plt.show() + print("Model prediction:", preds[i]) -- 2.34.1 From e8cc2e86e4495cec5b5aa469e4ca971621633762 Mon Sep 17 00:00:00 2001 From: p5tm2eri3 Date: Thu, 5 May 2022 17:44:56 +0800 Subject: [PATCH 2/6] Update README.md --- README.md | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/README.md b/README.md index 5cfe3b4..c4dcd66 100644 --- a/README.md +++ b/README.md @@ -108,17 +108,3 @@ model.evaluate(input_fn) # 预测单个图像 n_images = 6 -# 从数据集得到测试图像 -test_images = mnist.test.images[:n_images] -# 准备输入数据 -input_fn = tf.estimator.inputs.numpy_input_fn( - x={'images': test_images}, shuffle=False) -# 用训练好的模型预测图片类别 -preds = list(model.predict(input_fn)) - -# 可视化显示 -for i in range(n_images): - plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') - plt.show() - print("Model prediction:", preds[i]) - -- 2.34.1 From 31fb47ee0c6fee7be8d45d345b41fc21a33b3529 Mon Sep 17 00:00:00 2001 From: ps3pfn8w2 <3078518198@qq.com> Date: Thu, 5 May 2022 17:55:18 +0800 Subject: [PATCH 3/6] Update README.md --- README.md | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/README.md b/README.md index c4dcd66..45f0655 100644 --- a/README.md +++ b/README.md @@ -108,3 +108,45 @@ model.evaluate(input_fn) # 预测单个图像 n_images = 6 +# 从数据集得到测试图像 +test_images = mnist.test.images[:n_images] +# 准备输入数据 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': test_images}, shuffle=False) +# 用训练好的模型预测图片类别 +preds = list(model.predict(input_fn)) + +# 可视化显示 +for i in range(n_images): + plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') + plt.show() + print("Model prediction:", preds[i]) + + + # 从数据集得到测试图像 +test_images = mnist.test.images[:n_images] +# 准备输入数据 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': test_images}, shuffle=False) +# 用训练好的模型预测图片类别 +preds = list(model.predict(input_fn)) + +# 可视化显示 +for i in range(n_images): + plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') + plt.show() + print("Model prediction:", preds[i]) + +# 从数据集得到测试图像 +test_images = mnist.test.images[:n_images] +# 准备输入数据 +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': test_images}, shuffle=False) +# 用训练好的模型预测图片类别 +preds = list(model.predict(input_fn)) + +# 可视化显示 +for i in range(n_images): + plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') + plt.show() + print("Model prediction:", preds[i]) -- 2.34.1 From 12351bc1d75356047d27754b66554e60440febc4 Mon Sep 17 00:00:00 2001 From: plyxc258w <1136846615@qq.com> Date: Thu, 5 May 2022 18:05:05 +0800 Subject: [PATCH 4/6] Update README.md --- README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/README.md b/README.md index 45f0655..3c99ad0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,30 @@ # pattern_recognition from __future__ import division, print_function, absolute_import +# Import MNIST data，MNIST数据集导入 +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("MNIST_data", one_hot=False) + +import tensorflow as tf +import matplotlib.pyplot as plt +import numpy as np + +# Training Parameters，超参数 +learning_rate = 0.001 #学习率 +num_steps = 2000 # 训练步数 +batch_size = 128 # 训练数据批的大小 +# Network Parameters，网络参数 +num_input = 784 # MNIST数据输入 (img shape: 28*28) +num_classes = 10 # MNIST所有类别 (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25 + +# Create the neural network，创建深度神经网络 +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + + # Define a scope for reusing the variables，确定命名空间 + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator类型的输入为像素 + x = x_dict['images'] # Import MNIST data，MNIST数据集导入 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data", one_hot=False) -- 2.34.1 From 2be18e4b9853cc302a319881bad82f6dd123d58d Mon Sep 17 00:00:00 2001 From: p5tm2eri3 Date: Thu, 5 May 2022 18:12:13 +0800 Subject: [PATCH 5/6] Update README.md --- README.md | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/README.md b/README.md index 3c99ad0..de770d9 100644 --- a/README.md +++ b/README.md @@ -38,23 +38,7 @@ learning_rate = 0.001 #学习率 num_steps = 2000 # 训练步数 batch_size = 128 # 训练数据批的大小 -# Network Parameters，网络参数 -num_input = 784 # MNIST数据输入 (img shape: 28*28) -num_classes = 10 # MNIST所有类别 (0-9 digits) -dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25 - -# Create the neural network，创建深度神经网络 -def conv_net(x_dict, n_classes, dropout, reuse, is_training): - - # Define a scope for reusing the variables，确定命名空间 - with tf.variable_scope('ConvNet', reuse=reuse): - # TF Estimator类型的输入为像素 - x = x_dict['images'] - # MNIST数据输入格式为一位向量，包含784个特征 (28*28像素) - # 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数] - # 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数] - x = tf.reshape(x, shape=[-1, 28, 28, 1]) # 卷积层，32个卷积核，尺寸为5x5 conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) -- 2.34.1 From c39e527d1ad2e1d967cfee9ceeb155d4c8dada4a Mon Sep 17 00:00:00 2001 From: p9n6cg2k8 Date: Thu, 5 May 2022 18:15:19 +0800 Subject: [PATCH 6/6] Update README.md --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/README.md b/README.md index de770d9..4f888fe 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,23 @@ import numpy as np learning_rate = 0.001 #学习率 num_steps = 2000 # 训练步数 batch_size = 128 # 训练数据批的大小 +# Network Parameters，网络参数 +num_input = 784 # MNIST数据输入 (img shape: 28*28) +num_classes = 10 # MNIST所有类别 (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25 + +# Create the neural network，创建深度神经网络 +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + + # Define a scope for reusing the variables，确定命名空间 + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator类型的输入为像素 + x = x_dict['images'] + # MNIST数据输入格式为一位向量，包含784个特征 (28*28像素) + # 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数] + # 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) # 卷积层，32个卷积核，尺寸为5x5 -- 2.34.1