TF之CNN：利用sklearn(自帶手寫數字圖片識別數據集)使用dropout解決學習中overfitting的問題+Tensorboard顯示變化曲線

?

?

目錄

輸出結果

設計代碼

---

?

?

?

輸出結果

?

?

設計代碼

?

import tensorflow as tf from sklearn.datasets import load_digits #from sklearn.cross_validation import train_test_split from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer# load data digits = load_digits() X = digits.data y = digits.target y = LabelBinarizer().fit_transform(y) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):# add one more layer and return the output of this layerWeights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )Wx_plus_b = tf.matmul(inputs, Weights) + biases# here to dropoutWx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob) if activation_function is None:outputs = Wx_plus_belse:outputs = activation_function(Wx_plus_b, )tf.summary.histogram(layer_name + '/outputs', outputs) return outputs# define placeholder for inputs to network keep_prob = tf.placeholder(tf.float32) xs = tf.placeholder(tf.float32, [None, 64]) ys = tf.placeholder(tf.float32, [None, 10])# add output layer l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh) prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax) # the loss between prediction and real data cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1])) tf.summary.scalar ('loss', cross_entropy) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.Session() merged = tf.summary.merge_all() # summary writer goes in here train_writer = tf.summary.FileWriter("logs4/train", sess.graph) test_writer = tf.summary.FileWriter("logs4/test", sess.graph) sess.run(tf.global_variables_initializer()) for i in range(500): # here to determine the keeping probabilitysess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5}) if i % 50 == 0:# record losstrain_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})train_writer.add_summary(train_result, i) test_writer.add_summary(test_result, i)

?

相關文章
TF：利用sklearn自帶數據集使用dropout解決學習中overfitting的問題+Tensorboard顯示變化曲線
?

總結

以上是生活随笔為你收集整理的TF之CNN：利用sklearn(自带手写数字图片识别数据集)使用dropout解决学习中overfitting的问题+Tensorboard显示变化曲线的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。