tf.cast()：bool->数字
re = tf.cast([True,False],tf.float32) sess = tf.Session() sess.run(re) # Out[6]: # array([ 1., 0.], dtype=float32)
tf.argmax：(list，维度)
re = tf.argmax([[0,0.5]],1) sess.run(re) # Out[20]: # array([1])
tf.Variable() # 声明变量
tf.get_variable() # 声明变量
tf.global_variables_initializer() # 激活变量
tf.summary.histogram(layer_name+‘/weights‘,Weights) # 张量记录 tf.summary.scalar = (‘loss‘,cross_entropy) # 标量记录 merged = tf.summary.merge_all() # 记录激活 train_writer = tf.summary.FileWriter(‘logs/train‘,sess.graph) # 书写器生成
train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob:1}) # run记录
train_writer.add_summary(train_result,i) # 书写记录
| 操作组 | 操作 |
|---|---|
| Maths | Add, Sub, Mul, Div, Exp, Log, Greater, Less, Equal |
| Array | Concat, Slice, Split, Constant, Rank, Shape, Shuffle |
| Matrix | MatMul, MatrixInverse, MatrixDeterminant |
| Neuronal Network | SoftMax, Sigmoid, ReLU, Convolution2D, MaxPool |
| Checkpointing | Save, Restore |
| Queues and syncronizations | Enqueue, Dequeue, MutexAcquire, MutexRelease |
| Flow control | Merge, Switch, Enter, Leave, NextIteration |
| 操作 | 描述 |
|---|---|
| tf.add(x, y, name=None) | 求和 |
| tf.sub(x, y, name=None) | 减法 |
| tf.mul(x, y, name=None) | 乘法 |
| tf.div(x, y, name=None) | 除法 |
| tf.mod(x, y, name=None) | 取模 |
| tf.abs(x, name=None) | 求绝对值 |
| tf.neg(x, name=None) | 取负 (y = -x). |
| tf.sign(x, name=None) | 返回符号 y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0. |
| tf.inv(x, name=None) | 取反 |
| tf.square(x, name=None) | 计算平方 (y = x * x = x^2). |
| tf.round(x, name=None) | 舍入最接近的整数 # ‘a’ is [0.9, 2.5, 2.3, -4.4] tf.round(a) ==> [ 1.0, 3.0, 2.0, -4.0 ] |
| tf.sqrt(x, name=None) | 开根号 (y = \sqrt{x} = x^{1/2}). |
| tf.pow(x, y, name=None) | 幂次方 # tensor ‘x’ is [[2, 2], [3, 3]] # tensor ‘y’ is [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]] |
| tf.exp(x, name=None) | 计算e的次方 |
| tf.log(x, name=None) | 计算log，一个输入计算e的ln，两输入以第二输入为底 |
| tf.maximum(x, y, name=None) | 返回最大值 (x > y ? x : y) |
| tf.minimum(x, y, name=None) | 返回最小值 (x < y ? x : y) |
| tf.cos(x, name=None) | 三角函数cosine |
| tf.sin(x, name=None) | 三角函数sine |
| tf.tan(x, name=None) | 三角函数tan |
| tf.atan(x, name=None) | 三角函数ctan |
| 操作 | 描述 |
|---|---|
| tf.string_to_number (string_tensor, out_type=None, name=None) |
字符串转为数字 |
| tf.to_double(x, name=’ToDouble’) | 转为64位浮点类型–float64 |
| tf.to_float(x, name=’ToFloat’) | 转为32位浮点类型–float32 |
| tf.to_int32(x, name=’ToInt32’) | 转为32位整型–int32 |
| tf.to_int64(x, name=’ToInt64’) | 转为64位整型–int64 |
| tf.cast(x, dtype, name=None) | 将x或者x.values转换为dtype # tensor a is [1.8, 2.2], dtype=tf.floattf.cast(a, tf.int32) ==> [1, 2] # dtype=tf.int32 |
原文：http://www.cnblogs.com/hellcat/p/6905878.html