#優化一個乘法算子

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#coding:utf-8 __author__ = 'similarface' import tensorflow as tf sess=tf.Session() #創建一個常量張量 a=tf.Variable(tf.constant(4.)) x_val=5. x_data=tf.placeholder(dtype=tf.float32)#添加計算圖 multiplication=tf.multiply(a,x_data) #我們將聲明損失函數為輸出與期望目標值100之間的L2距離： loss = tf.square(tf.subtract(multiplication, 100.))#初始化模型變量 現在我們并將我們的優化算法聲明為標準梯度下降： init = tf.initialize_all_variables() sess.run(init) #標準梯度下降 my_opt = tf.train.GradientDescentOptimizer(0.01) train_step = my_opt.minimize(loss)print('優化乘法輸出100.') for i in range(10):sess.run(train_step, feed_dict={x_data: x_val})a_val = sess.run(a)mult_output = sess.run(multiplication, feed_dict={x_data: x_val})print(str(a_val) + ' * ' + str(x_val) + ' = ' + str(mult_output))

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__author__ = 'similarface' from tensorflow.python.framework import ops import tensorflow as tf ''' y=a*x+b ''' ops.reset_default_graph() sess = tf.Session() a = tf.Variable(tf.constant(1.)) b = tf.Variable(tf.constant(1.)) x_val = 5. x_data = tf.placeholder(dtype=tf.float32) two_gate = tf.add(tf.multiply(a, x_data), b) loss = tf.square(tf.subtract(two_gate, 50.)) my_opt = tf.train.GradientDescentOptimizer(0.01) train_step = my_opt.minimize(loss) init = tf.initialize_all_variables() sess.run(init) print('\nOptimizing Two Gate Output to 50.') for i in range(10):a_val, b_val = (sess.run(a), sess.run(b))# Run the train stepsess.run(train_step, feed_dict={x_data: x_val})# Get the a and b valuesa_val, b_val = (sess.run(a), sess.run(b))# Run the two-gate graph outputtwo_gate_output = sess.run(two_gate, feed_dict={x_data: x_val})print(str(a_val) + ' * ' + str(x_val) + ' + ' + str(b_val) + '= ' + str(two_gate_output))

'''
result:

10.4 * 5.0 + 2.88= 54.88
14.912 * 5.0 + 3.7824= 78.3424
17.0778 * 5.0 + 4.21555= 89.6043
18.1173 * 5.0 + 4.42347= 95.0101
18.6163 * 5.0 + 4.52326= 97.6048
18.8558 * 5.0 + 4.57117= 98.8503
18.9708 * 5.0 + 4.59416= 99.4482
19.026 * 5.0 + 4.6052= 99.7351
19.0525 * 5.0 + 4.61049= 99.8729
19.0652 * 5.0 + 4.61304= 99.939

''' #coding:utf-8 __author__ = 'similarface' ''' 使用 Placeholders and VariablesVariables 變量是tensorflow 會跟蹤并優化Placeholders 占位符 類型，維度 占用 ''' import tensorflow as tf import numpy as npl_var=tf.Variable(tf.zeros([2,3])) sess=tf.Session() init_all=tf.global_variables_initializer() sess.run(init_all) print(l_var)x=tf.placeholder(tf.float32,shape=[2,2]) #identity x=y y=tf.identity(x) x_vals=np.random.rand(2,2) #Placeholders 需要喂入數據 sess.run(y,feed_dict={x:x_vals}) print(y)#返回一個給定對角值的對角tensor ''' 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 ''' id_matrix=tf.diag([1.0,1.0,1.0])#tf.truncated_normal(shape, mean, stddev) :shape表示生成張量的維度，mean是均值，stddev是標準差。這個函數產生正太分布，均值和標準差自己設定 A=tf.truncated_normal([2,3]) #指定值填充矩陣 B = tf.fill([2,3], 5.0) #均勻分布 C = tf.random_uniform([3,2]) #將np數組轉化成tensor D = tf.convert_to_tensor(np.array([[1., 2., 3.],[-3., -7.,-1.],[0., 5., -2.]]))print("tf.diag: \n",sess.run(id_matrix))print("truncated_normal: 2-2\n",sess.run(A))print('fill:\n',sess.run(B))print('random_uniform:\n',sess.run(C))print('convert_to_tensor:\n',sess.run(D))print("A+B\n",sess.run(A+B))print('C:\n',sess.run(C)) #C轉置 print("C'T:\n",sess.run(tf.transpose(C)))#行列式 print(sess.run(tf.matrix_determinant(D)))#就是得到逆矩陣 print(sess.run(tf.matrix_inverse(D)))#對稱正定矩陣 print(sess.run(tf.cholesky(id_matrix)))#求解特征值和特征向量 print(sess.run(D)) print(sess.run(tf.self_adjoint_eig(D)))

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轉載于:https://www.cnblogs.com/similarface/p/8579537.html

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