MNIST by TensorFlow

Author: issa1106

practice20.py

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+from tensorflow.examples.tutorials.mnist import input_data
+import numpy
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+# 讀入手寫數字辨識資料 會下載gz檔
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+x_train = mnist.train.images
+y_train = mnist.train.labels
+x_test = mnist.test.images
+y_test = mnist.test.labels
+
+print(x_train.shape)  # 55000張 28x28=784像素的圖片
+
+# 印出圖片看看 第54999張=最後一張
+last_train_img = numpy.reshape(x_train[54999, :], (28,28))
+plt.matshow(last_train_img, cmap=plt.get_cmap('gray'))
+plt.show()
+
+# 設定參數
+learning_rate = 0.5
+training_steps = 1000
+batch_size = 100
+logs_path = 'TensorBoard/'
+n_features = x_train.shape[1]
+n_labels = y_train.shape[1]
+
+# 建立Feeds
+with tf.name_scope("Inputs"):
+	x = tf.placeholder(tf.float32, [None, n_features], name="Input_Data")
+with tf.name_scope("Labels"):
+	y = tf.placeholder(tf.float32, [None, n_labels], name="Labe_Data")
+
+# 建立 Variables
+with tf.name_scope("ModelParameters"):
+	W = tf.Variable(tf.zeros([n_features, n_labels]), name="Weights")
+	b = tf.Variable(tf.zeros([n_labels]), name="Biases")
+
+# 深度學習模型
+with tf.name_scope("Model"):
+	prediction = tf.nn.softmax(tf.matmul(x, W) + b)
+with tf.name_scope("Cross_Entropy"):  # 定義Loss
+	loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(prediction),
+		reduction_indices=1))
+	tf.summary.scalar("Loss", loss)
+with tf.name_scope("Gradient_Descent"):  # 優化Optimization 梯度下降
+	optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
+with tf.name_scope("Accuracy"):  # 準確度
+	correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
+	acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+	tf.summary.scalar("Accuracy", acc)
+
+# 初始化
+init = tf.global_variables_initializer()
+
+sess = tf.Session()
+sess.run(init)
+
+merged = tf.summary.merge_all()
+writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
+
+for step in range(training_steps):
+	batch_xs, batch_ys = mnist.train.next_batch(batch_size)
+	sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
+	if step % 50 == 0:
+		print(sess.run(loss, feed_dict={x: batch_xs, y: batch_ys}))
+		summary = sess.run(merged, feed_dict={x:batch_xs, y: batch_ys})
+		writer.add_summary(summary, step)
+
+print("Accuracy:", sess.run(acc, feed_dict={x: x_test, y: y_test}))
+
+sess.close()