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
目录
- 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 layer
- Weights = 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 dropout
- Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)
- if activation_function is None:
- outputs = Wx_plus_b
- else:
- 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 probability
- sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
- if i % 50 == 0:
- record loss
- train_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)
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TF:利用sklearn自带数据集使用dropout解决学习中overfitting的问题+Tensorboard显示变化曲线
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