TensorFlow 官网API
tf.summary.scalar
tf.summary.FileWriter
tf.summary.histogram
tf.summary.merge_all
tf.equal
tf.argmax
tf.cast
tf.div(x, y, name=None)
tf.pow(x, y, name=None)
tf.unstack(value, num=None, axis=0, name='unstack')
tf.stack(values, axis=0, name='stack')
tf.transpose(a, perm=None, name='transpose')
tf.set_random_seed(seed)
tf.reshape(tensor, shape, name=None)
tf.multiply(x, y, name=None
tf.name_scope(args,*kwds)
tf.variable_scope(args,*kwds)
class tf.contrib.rnn.BasicLSTMCell
tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)
tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)
tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)
tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None)
tf.gradients
apply_gradients
tf.summary.scalar
https://www.tensorflow.org/api_docs/python/tf/summary/scalar
tf.summary.FileWriter:
https://www.tensorflow.org/api_docs/python/tf/summary/FileWriter
tf.summary.histogram
https://www.tensorflow.org/api_docs/python/tf/summary/histogram
tf.summary.merge_all
https://www.tensorflow.org/api_docs/python/tf/summary/merge_all
tf.equal：
https://www.tensorflow.org/api_docs/python/tf/equal
tf.argmax：
https://www.tensorflow.org/api_docs/python/tf/argmax
tf.cast
https://www.tensorflow.org/api_docs/python/tf/cast
tf.div(x, y, name=None)
參考鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/div
tf.pow(x, y, name=None)
參考鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/pow
tf.unstack(value, num=None, axis=0, name='unstack')
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/unstack
tf.stack(values, axis=0, name='stack')
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/stack
1 ###tf.stack()/unstack(): 2 import tensorflow as tf 3 4 a = tf.constant([1, 2, 3]) 5 b = tf.constant([4, 5, 6]) 6 c = tf.stack([a, b], axis=0) 7 d = tf.stack([a, b], axis=1) 8 e = tf.unstack(c, axis=0) 9 f = tf.unstack(c, axis=1) 10 with tf.Session() as sess: 11 print(sess.run(c)) 12 print(sess.run(d)) 13 print(sess.run(e)) 14 print(sess.run(f))
[[1 2 3] [4 5 6]] [[1 4] [2 5] [3 6]] [array([1, 2, 3], dtype=int32), array([4, 5, 6], dtype=int32)] [array([1, 4], dtype=int32), array([2, 5], dtype=int32), array([3, 6], dtype=int32)]
參考鏈接：https://blog.csdn.net/u012193416/article/details/77411535
1 ###tf.stack()/unstack(): 2 import tensorflow as tf 3 4 g = tf.constant([[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]],[[13, 14, 15, 16],[17, 18, 19, 20],[21, 22, 23, 24]]]) 5 h = tf.unstack(g) 6 with tf.Session() as sess: 7 print(sess.run(h))
[array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]], dtype=int32),
array([[13, 14, 15, 16],
[17, 18, 19, 20],
[21, 22, 23, 24]], dtype=int32)]
tf.transpose(a, perm=None, name='transpose')
官方鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/transpose
1 ###tf.transpose() 2 import tensorflow as tf 3 4 a = tf.constant([[1, 2, 3], 5 [4, 5, 6]]) 6 b = tf.constant([[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]],[[13, 14, 15, 16],[17, 18, 19, 20],[21, 22, 23, 24]]]) 7 c = tf.transpose(a, [0, 1]) 8 d = tf.transpose(a, [1, 0]) 9 e = tf.transpose(b, [0, 1, 2]) 10 f = tf.transpose(b, [1, 0, 2]) 11 g = tf.transpose(b, [0, 2, 1]) 12 with tf.Session() as sess: 13 print(sess.run(c)) 14 print(sess.run(d)) 15 print(sess.run(e)) 16 print(sess.run(f)) 17 print(sess.run(g))
[[1 2 3] [4 5 6]] [[1 4] [2 5] [3 6]] [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]] [[[ 1 2 3 4] [13 14 15 16]] [[ 5 6 7 8] [17 18 19 20]] [[ 9 10 11 12] [21 22 23 24]]] [[[ 1 5 9] [ 2 6 10] [ 3 7 11] [ 4 8 12]] [[13 17 21] [14 18 22] [15 19 23] [16 20 24]]]
博客鏈接：https://www.cnblogs.com/studyDetail/p/6533316.html
tf.set_random_seed(seed)
實例運行參見: Jupyter notebook:TensorFlowAPI
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/set_random_seed
tf.reshape(tensor, shape, name=None)
Args:
tensor: ATensor.
shape: ATensor. Must be one of the following types:int32,int64. Defines the shape of the output tensor.
name: A name for the operation (optional).
mport tensorflow as tf
t = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9])
m = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
n = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
with tf.Session() as sess:
print('t->[3, 3]:
', sess.run(tf.reshape(t, [3,3 ])), '
')
print('m->[2, 4]:
', sess.run(tf.reshape(m, [2,4 ])), '
')
print('n->[3, 2, 3]:
', sess.run(tf.reshape(n, [3, 2, 3 ])), '
')
print('n->[2, -1]:
', sess.run(tf.reshape(n, [2, -1])), '
')
print('n->[-1, 9]:
', sess.run(tf.reshape(n, [-1, 9])), '
')
print('n->[2, -1, 3]:
', sess.run(tf.reshape(n, [2, -1, 3])), '
')
t->[3, 3]: [[1 2 3] [4 5 6] [7 8 9]] m->[2, 4]: [[1 2 3 4] [5 6 7 8]] n->[3, 2, 3]: [[[ 1 2 3] [ 4 5 6]] [[ 7 8 9] [10 11 12]] [[13 14 15] [16 17 18]]] n->[2, -1]: [[ 1 2 3 4 5 6 7 8 9] [10 11 12 13 14 15 16 17 18]] n->[-1, 9]: [[ 1 2 3 4 5 6 7 8 9] [10 11 12 13 14 15 16 17 18]] n->[2, -1, 3]: [[[ 1 2 3] [ 4 5 6] [ 7 8 9]] [[10 11 12] [13 14 15] [16 17 18]]]
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/reshape
tf.multiply(x, y, name=None
import tensorflow as tf
#兩個矩陣相乘
x=tf.constant([[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]])
y=tf.constant([[0,0,1.0],[0,0,1.0],[0,0,1.0]])
#注意這里這里x,y要有相同的數據類型，不然就會因為數據類型不匹配而出錯
z=tf.multiply(x,y)
#兩個數相乘
x1=tf.constant(1)
y1=tf.constant(2)
#注意這里這里x1,y1要有相同的數據類型，不然就會因為數據類型不匹配而出錯
z1=tf.multiply(x1,y1)
#數和矩陣相乘
x2=tf.constant([[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]])
y2=tf.constant(2.0)
#注意這里這里x1,y1要有相同的數據類型，不然就會因為數據類型不匹配而出錯
z2=tf.multiply(x2,y2)
with tf.Session() as sess:
print(sess.run(z))
print(sess.run(z1))
print(sess.run(z2))
[[0. 0. 3.] [0. 0. 3.] [0. 0. 3.]] 2 [[2. 4. 6.] [2. 4. 6.] [2. 4. 6.]]
https://blog.csdn.net/m0_37041325/article/details/77036513
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/multiply
tf.name_scope(args,*kwds)
Returns a context manager for use when defining a Python op.
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/name_scope
tf.variable_scope(args,*kwds)
Returns a context manager for defining ops that creates variables (layers).
This context manager validates that the (optional)valuesare from the same graph, ensures that graph is the default graph, and pushes a name scope and a variable scope.
https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/variable_scope
class tf.contrib.rnn.BasicLSTMCell
鏈接：https://tensorflow.google.cn/versions/r1.9/api_docs/python/tf/contrib/rnn/BasicLSTMCell
tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)
官方鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/nn/dynamic_rnn
鏈接：https://github.com/MorvanZhou/tutorials/blob/master/tensorflowTUT/tf20_RNN2/full_code.py
tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)
鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/nn/softmax_cross_entropy_with_logits
tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)
鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/nn/moments
tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None)
Weighted cross-entropy loss for a sequence of logits (per example).
鏈接：https://tensorflow.google.cn/versions/r1.0/api_docs/python/tf/contrib/legacy_seq2seq/sequence_loss_by_example
tf.distributions.Normal
Aliases:
Classtf.contrib.distributions.Normal
Classtf.distributions.Normal
The Normal distribution with locationlocandscaleparameters.
whereloc = muis the mean,scale = sigmais the std. deviation, and,Zis the normalization constant.
Methods:
總結
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