Add the overfitting test program.

tf_study/overfitting.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+# _*_ coding:utf-8 _*_
+
+import tensorflow as tf
+from sklearn.datasets import load_digits
+from sklearn.cross_validation import train_test_split
+from sklearn.preprocessing import LabelBinarizer
+
+def add_layer(inputs, in_size, out_size, layer_name, dropout = 1, activiation_function = None):
+	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
+
+	# Dropout
+	Wx_plus_b = tf.nn.dropout(Wx_plus_b, dropout)
+
+	if activiation_function is None:
+		outputs = Wx_plus_b
+	else:
+		outputs = activiation_function(Wx_plus_b)
+
+	tf.summary.histogram(layer_name + '/outputs', outputs)
+	return outputs
+
+if __name__ == '__main__':
+	# 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)
+
+	# Define the placeholder for inputs to network
+	keep_prob = tf.placeholder(tf.float32)
+	xs = tf.placeholder(tf.float32, [None, 64]) # 8 * 8
+	ys = tf.placeholder(tf.float32, [None, 10])
+
+	# Add output layer
+	l1 = add_layer(xs, 64, 50, 'l1', keep_prob, activiation_function = tf.nn.tanh)
+	prediction = add_layer(l1, 50, 10, 'l2', keep_prob, activiation_function = tf.nn.softmax)
+
+	# The loss
+	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()
+
+	train_writer = tf.summary.FileWriter("logs/train", sess.graph)
+	test_writer = tf.summary.FileWriter("logs/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 the 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)
+

tf_study/tf_test.py

@@ -143,4 +143,4 @@ def main(_):
 	)
 
 	FLAGS, unparsed = parser.parse_known_args()
-	tf.app.run(main = main, argv = [sys.argv[0]] + unparsed)
+	tf.app.run(main = main, argv = [sys.argv[0]] + unparsed)