資源簡介
本資源包含一個Mnist手寫體的訓練腳本,可在環境配置好的情況下直接訓練學習模型,然后可以根據模型輸入任意照片,預測結果,直接可用,適合入門者。

代碼片段和文件信息
#?Copyright?2016?Niek?Temme.
#?Adapted?form?the?on?the?MNIST?expert?tutorial?by?Google.?
#
#?Licensed?under?the?Apache?License?Version?2.0?(the?“License“);
#?you?may?not?use?this?file?except?in?compliance?with?the?License.
#?You?may?obtain?a?copy?of?the?License?at
#
#?????http://www.apache.org/licenses/LICENSE-2.0
#
#?Unless?required?by?applicable?law?or?agreed?to?in?writing?software
#?distributed?under?the?License?is?distributed?on?an?“AS?IS“?BASIS
#?WITHOUT?WARRANTIES?OR?CONDITIONS?OF?ANY?KIND?either?express?or?implied.
#?See?the?License?for?the?specific?language?governing?permissions?and
#?limitations?under?the?License.
#?==============================================================================
“““A?very?simple?MNIST?classifier.
Documentation?at
http://niektemme.com/?@@to?do
This?script?is?based?on?the?Tensoflow?MNIST?expert?tutorial
See?extensive?documentation?for?the?tutorial?at
https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html
“““
#import?modules
import?os
import?tensorflow?as?tf
from?tensorflow.examples.tutorials.mnist?import?input_data
#import?data
mnist?=?input_data.read_data_sets(“../data/“?one_hot=True)
sess?=?tf.InteractiveSession()
#?Create?the?model
x?=?tf.placeholder(tf.float32?[None?784])
y_?=?tf.placeholder(tf.float32?[None?10])
W?=?tf.Variable(tf.zeros([784?10]))
b?=?tf.Variable(tf.zeros([10]))
y?=?tf.nn.softmax(tf.matmul(x?W)?+?b)
def?weight_variable(shape):
??initial?=?tf.truncated_normal(shape?stddev=0.1)
??return?tf.Variable(initial)
def?bias_variable(shape):
??initial?=?tf.constant(0.1?shape=shape)
??return?tf.Variable(initial)
def?conv2d(x?W):
??return?tf.nn.conv2d(x?W?strides=[1?1?1?1]?padding=‘SAME‘)
def?max_pool_2x2(x):
??return?tf.nn.max_pool(x?ksize=[1?2?2?1]
????????????????????????strides=[1?2?2?1]?padding=‘SAME‘)
W_conv1?=?weight_variable([5?5?1?32])
b_conv1?=?bias_variable([32])
x_image?=?tf.reshape(x?[-128281])
h_conv1?=?tf.nn.relu(conv2d(x_image?W_conv1)?+?b_conv1)
h_pool1?=?max_pool_2x2(h_conv1)
W_conv2?=?weight_variable([5?5?32?64])
b_conv2?=?bias_variable([64])
h_conv2?=?tf.nn.relu(conv2d(h_pool1?W_conv2)?+?b_conv2)
h_pool2?=?max_pool_2x2(h_conv2)
W_fc1?=?weight_variable([7?*?7?*?64?1024])
b_fc1?=?bias_variable([1024])
h_pool2_flat?=?tf.reshape(h_pool2?[-1?7*7*64])
h_fc1?=?tf.nn.relu(tf.matmul(h_pool2_flat?W_fc1)?+?b_fc1)
keep_prob?=?tf.placeholder(tf.float32)
h_fc1_drop?=?tf.nn.dropout(h_fc1?keep_prob)
W_fc2?=?weight_variable([1024?10])
b_fc2?=?bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop?W_fc2)?+?b_fc2)
#?Define?loss?and?optimizer
cross_entropy?=?-tf.reduce_sum(y_*tf.log(y_conv))
train_step?=?tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction?=?tf.equal(tf.argmax(y_conv1)?tf.argmax(y_1))
accuracy?=?tf.reduce_mean(tf.cast(correct_prediction?tf.float32))
“““
Train?the?model?and?save?the?model?to?disk?as?a?model2.ckpt?file
file?is?stored?in?the?same?directory?as?this?
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????3979??2017-11-07?11:47??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\create_model_2.py
?????文件????????5227??2017-11-07?13:46??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\predict_2.py
?????文件?????7840016??1998-01-26?23:07??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\t10k-images.idx3-ubyte
?????文件???????10008??1998-01-26?23:07??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\t10k-labels.idx1-ubyte
?????文件????47040016??1996-11-18?23:36??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\train-images.idx3-ubyte
?????文件???????60008??1996-11-18?23:36??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\train-labels.idx1-ubyte
?????目錄???????????0??2017-11-07?20:06??Mnist手寫體訓練腳本和測試腳本(含訓練集和測試集)\
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