tensorflow编程: Neural Network
tensorflow编程: Neural Network
JNingWei
发布于 2018-09-28 15:23:25
发布于 2018-09-28 15:23:25
Activation Functions
tf.nn.relu
负数归零。
tf.nn.relu6
负数归零，大于6的正数归6。
tf.nn.crelu
对 features 和 tf.negative(features) 分别 Relu 并 concatenate 在一起。
tf.nn.elu
负数进行 exp(features) - 1 。
tf.nn.softplus
返回 log(exp(features) + 1) 。
tf.nn.softsign
返回 features / (abs(features) + 1) 。
tf.nn.dropout
根据 keep_prob参数项 随机进行 dropout 。
tf.nn.bias_add
加 偏置项 。
tf.sigmoid
返回 1 / (1 + exp(-x)) 。
tf.tanh
返回 tanh(x) 。
# coding=utf-8
import tensorflow as tf
# 保证每次都是 同一组features
import numpy as np
inputs = np.random.uniform(-10, 10, size=[3, 3])
features = tf.placeholder_with_default(input=inputs, shape=[3, 3])
# 激活函数
output_relu = tf.nn.relu(features)
output_relu6 = tf.nn.relu6(features)
output_crelu = tf.nn.crelu(features)
output_elu = tf.nn.elu(features)
output_softplus = tf.nn.softplus(features)
output_softsign = tf.nn.softsign(features)
output_dropout = tf.nn.dropout(features, keep_prob=0.5)
output_bias_add = tf.nn.bias_add(features, bias=[10, 10, 10])
output_sigmoid = tf.nn.sigmoid(features)
output_tanh = tf.nn.tanh(features)
with tf.Session() as sess:
print '\nfeatures :\n', sess.run(features)
print '\n----------\n'
print '\ntf.nn.relu :\n', sess.run(output_relu)
print '\ntf.nn.relu6 :\n', sess.run(output_relu6)
print '\ntf.nn.crelu :\n', sess.run(output_crelu)
print '\ntf.nn.elu :\n', sess.run(output_elu)
print '\ntf.nn.softplus :\n', sess.run(output_softplus)
print '\ntf.nn.softsign :\n', sess.run(output_softsign)
print '\ntf.nn.output_dropout :\n', sess.run(output_dropout)
print '\ntf.nn.output_bias_add :\n', sess.run(output_bias_add)
print '\ntf.nn.output_sigmoid :\n', sess.run(output_sigmoid)
print '\ntf.nn.output_tanh :\n', sess.run(output_tanh)features :
[[ 0.53874537 -3.09047282 -2.88714205]
[-1.92602402 -1.56025457 3.64309646]
[-9.13147387 8.37367913 -7.9849204 ]]
----------
tf.nn.relu :
[[ 0.53874537 0. 0. ]
[ 0. 0. 3.64309646]
[ 0. 8.37367913 0. ]]
tf.nn.relu6 :
[[ 0.53874537 0. 0. ]
[ 0. 0. 3.64309646]
[ 0. 6. 0. ]]
tf.nn.crelu :
[[ 0.53874537 0. 0. 0. 3.09047282 2.88714205]
[ 0. 0. 3.64309646 1.92602402 1.56025457 0. ]
[ 0. 8.37367913 0. 9.13147387 0. 7.9849204 ]]
tf.nn.elu :
[[ 0.53874537 -0.95451955 -0.94426473]
[-0.85427355 -0.78991742 3.64309646]
[-0.99989179 8.37367913 -0.99965944]]
tf.nn.softplus :
[[ 9.98370226e-01 4.44765358e-02 5.42374660e-02]
[ 1.36038893e-01 1.90688608e-01 3.66893104e+00]
[ 1.08200132e-04 8.37390997e+00 3.40501625e-04]]
tf.nn.softsign :
[[ 0.3501199 -0.75552948 -0.74274159]
[-0.65823931 -0.60941384 0.78462649]
[-0.90129768 0.8933183 -0.88870241]]
tf.nn.output_dropout :
[[ 0. -6.18094565 -5.77428411]
[ -0. -3.12050914 7.28619293]
[-18.26294775 16.74735827 -0. ]]
tf.nn.output_bias_add :
[[ 10.53874537 6.90952718 7.11285795]
[ 8.07397598 8.43974543 13.64309646]
[ 0.86852613 18.37367913 2.0150796 ]]
tf.nn.output_sigmoid :
[[ 6.31520510e-01 4.35019567e-02 5.27928497e-02]
[ 1.27191314e-01 1.73610121e-01 9.74496282e-01]
[ 1.08194278e-04 9.99769189e-01 3.40443661e-04]]
tf.nn.output_tanh :
[[ 0.49203767 -0.9958716 -0.9938064 ]
[-0.9584108 -0.91546169 0.99863108]
[-0.99999998 0.99999989 -0.99999977]]Convolution
tf.nn.convolution
tf.nn.convolution (input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)
计算 N-D卷积 的 和 。
tf.nn.conv2d
2-D 卷积
tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
对 4-D 的 input 和 filter 进行 tensor之间 的 2-D 卷积。
其中，input.shape = [batch, in_height, in_width, in_channels] ，filter.shape = [filter_height, filter_width, in_channels, out_channels]， stride 为长度为4的int型一维列表。
tf.nn.conv2d_transpose
2-D 卷积(tf.nn.conv2d) 的 转置
tf.nn.conv2d_transpose (value, filter, output_shape, strides, padding=’SAME’, data_format=’NHWC’, name=None)
这个操作有时在解卷积网络之后称为“反卷积”，但实际上是conv2d的转置（渐变），而不是实际的反卷积。
tf.nn.conv1d
tf.nn.conv1d (value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
其中， stride 为整型。
padding参数项 为 SAME 时：
# coding=utf-8
import tensorflow as tf
# 保证每次都是 同一组features
import numpy as np
x_4d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 10), dtype=np.float32), [1, 3, 3, 1]),
shape=[1, 3, 3, 1])
y_4d = tf.placeholder_with_default(input=np.ones(shape=[2, 2, 1, 1], dtype=np.float32),
shape=[2, 2, 1, 1])
x_3d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 4), dtype=np.float32), [1, 3, 1]),
shape=[1, 3, 1])
y_3d = tf.placeholder_with_default(input=np.ones(shape=[2, 1, 1], dtype=np.float32),
shape=[2, 1, 1])
# 激活函数; padding='SAME'
output_convolution = tf.nn.convolution(x_4d, y_4d, 'SAME')
output_conv2d = tf.nn.conv2d(x_4d, y_4d, [1, 1, 1, 1], 'SAME')
output_conv2d_transpose = tf.nn.conv2d_transpose(x_4d, y_4d, [1, 6, 6, 1], [1, 2, 2, 1], 'SAME')
output_conv1d = tf.nn.conv1d(x_3d, y_3d, 1, 'SAME')
with tf.Session() as sess:
print '\nx :\n', sess.run(x_4d)
print '\n----------\n'
print '\n\ntf.nn.convolution :\n', sess.run(output_convolution)
print '\n\ntf.nn.conv2d :\n', sess.run(output_conv2d)
print '\n\ntf.nn.conv2d_transpose :\n', sess.run(output_conv2d_transpose)
print '\n\ntf.nn.conv1d :\n', sess.run(output_conv1d)输出：
x :
[[[[ 1.]
[ 2.]
[ 3.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 7.]
[ 8.]
[ 9.]]]]
----------
tf.nn.convolution :
[[[[ 12.]
[ 16.]
[ 9.]]
[[ 24.]
[ 28.]
[ 15.]]
[[ 15.]
[ 17.]
[ 9.]]]]
tf.nn.conv2d :
[[[[ 12.]
[ 16.]
[ 9.]]
[[ 24.]
[ 28.]
[ 15.]]
[[ 15.]
[ 17.]
[ 9.]]]]
tf.nn.conv2d_transpose :
[[[[ 1.]
[ 1.]
[ 2.]
[ 2.]
[ 3.]
[ 3.]]
[[ 1.]
[ 1.]
[ 2.]
[ 2.]
[ 3.]
[ 3.]]
[[ 4.]
[ 4.]
[ 5.]
[ 5.]
[ 6.]
[ 6.]]
[[ 4.]
[ 4.]
[ 5.]
[ 5.]
[ 6.]
[ 6.]]
[[ 7.]
[ 7.]
[ 8.]
[ 8.]
[ 9.]
[ 9.]]
[[ 7.]
[ 7.]
[ 8.]
[ 8.]
[ 9.]
[ 9.]]]]
tf.nn.conv1d :
[[[ 3.]
[ 5.]
[ 3.]]]
padding参数项 为 VALID 时：
# coding=utf-8
import tensorflow as tf
# 保证每次都是 同一组features
import numpy as np
x_4d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 10), dtype=np.float32), [1, 3, 3, 1]),
shape=[1, 3, 3, 1])
y_4d = tf.placeholder_with_default(input=np.ones(shape=[2, 2, 1, 1], dtype=np.float32),
shape=[2, 2, 1, 1])
x_3d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 4), dtype=np.float32), [1, 3, 1]),
shape=[1, 3, 1])
y_3d = tf.placeholder_with_default(input=np.ones(shape=[2, 1, 1], dtype=np.float32),
shape=[2, 1, 1])
# 激活函数; padding='VALID'
output_convolution = tf.nn.convolution(x_4d, y_4d, 'VALID')
output_conv2d = tf.nn.conv2d(x_4d, y_4d, [1, 1, 1, 1], 'VALID')
output_conv2d_transpose = tf.nn.conv2d_transpose(x_4d, y_4d, [1, 6, 6, 1], [1, 2, 2, 1], 'VALID')
output_conv1d = tf.nn.conv1d(x_3d, y_3d, 1, 'VALID')
with tf.Session() as sess:
print '\nx :\n', sess.run(x_4d)
print '\n----------\n'
print '\n\ntf.nn.convolution :\n', sess.run(output_convolution)
print '\n\ntf.nn.conv2d :\n', sess.run(output_conv2d)
print '\n\ntf.nn.conv2d_transpose :\n', sess.run(output_conv2d_transpose)
print '\n\ntf.nn.conv1d :\n', sess.run(output_conv1d)输出：
x :
[[[[ 1.]
[ 2.]
[ 3.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 7.]
[ 8.]
[ 9.]]]]
----------
tf.nn.convolution :
[[[[ 12.]
[ 16.]]
[[ 24.]
[ 28.]]]]
tf.nn.conv2d :
[[[[ 12.]
[ 16.]]
[[ 24.]
[ 28.]]]]
tf.nn.conv2d_transpose :
[[[[ 1.]
[ 1.]
[ 2.]
[ 2.]
[ 3.]
[ 3.]]
[[ 1.]
[ 1.]
[ 2.]
[ 2.]
[ 3.]
[ 3.]]
[[ 4.]
[ 4.]
[ 5.]
[ 5.]
[ 6.]
[ 6.]]
[[ 4.]
[ 4.]
[ 5.]
[ 5.]
[ 6.]
[ 6.]]
[[ 7.]
[ 7.]
[ 8.]
[ 8.]
[ 9.]
[ 9.]]
[[ 7.]
[ 7.]
[ 8.]
[ 8.]
[ 9.]
[ 9.]]]]
tf.nn.conv1d :
[[[ 3.]
[ 5.]]]Pooling
tf.nn.avg_pool
输出 average_pooling 的结果 。
tf.nn.avg_pool (value, ksize, strides, padding, data_format=’NHWC’, name=None)
tf.nn.max_pool
输出 max_pooling 的结果 。
tf.nn.max_pool (value, ksize, strides, padding, data_format=’NHWC’, name=None)
tf.nn.max_pool_with_argmax
对 input 进行 max_pooling，输出 max_pooling 的结果 以及 max_pooling 的辅助indices（用于后面的up_sampling）。
tf.nn.max_pool_with_argmax (input, ksize, strides, padding, Targmax=None, name=None)
tf.nn.pool
padding='SAME' 时：
# coding=utf-8
import tensorflow as tf
# 保证每次都是 同一组features
import numpy as np
x_4d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 10), dtype=np.float32), [1, 3, 3, 1]),
shape=[1, 3, 3, 1])
y_4d = tf.placeholder_with_default(input=np.ones(shape=[2, 2, 1, 1], dtype=np.float32),
shape=[2, 2, 1, 1])
# padding='SAME'
output_avg_pool = tf.nn.avg_pool(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
output_max_pool = tf.nn.max_pool(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
output_max_pool_with_argmax = tf.nn.max_pool_with_argmax(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
with tf.Session() as sess:
print '\ninput :\n', sess.run(x_4d)
print '\n----------\n'
print '\n\ntf.nn.avg_pool :\n', sess.run(output_avg_pool)
print '\n\ntf.nn.max_pool :\n', sess.run(output_max_pool)
print '\n\ntf.nn.max_pool_with_argmax :\n', sess.run(output_max_pool_with_argmax)input :
[[[[ 1.]
[ 2.]
[ 3.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 7.]
[ 8.]
[ 9.]]]]
----------
tf.nn.avg_pool :
[[[[ 3. ]
[ 4.5]]
[[ 7.5]
[ 9. ]]]]
tf.nn.max_pool :
[[[[ 5.]
[ 6.]]
[[ 8.]
[ 9.]]]]
tf.nn.max_pool_with_argmax :
MaxPoolWithArgmax(output=array([[[[ 5.],
[ 6.]],
[[ 8.],
[ 9.]]]], dtype=float32), argmax=array([[[[4],
[5]],
[[7],
[8]]]]))
padding='VALID' 时：
# coding=utf-8
import tensorflow as tf
# 保证每次都是 同一组features
import numpy as np
x_4d = tf.placeholder_with_default(input=np.ndarray.reshape(np.array(range(1, 10), dtype=np.float32), [1, 3, 3, 1]),
shape=[1, 3, 3, 1])
y_4d = tf.placeholder_with_default(input=np.ones(shape=[2, 2, 1, 1], dtype=np.float32),
shape=[2, 2, 1, 1])
# padding='VALID'
output_avg_pool = tf.nn.avg_pool(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
output_max_pool = tf.nn.max_pool(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
output_max_pool_with_argmax = tf.nn.max_pool_with_argmax(x_4d, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
with tf.Session() as sess:
print '\nvalue :\n', sess.run(x_4d)
print '\n----------\n'
print '\n\ntf.nn.avg_pool :\n', sess.run(output_avg_pool)
print '\n\ntf.nn.max_pool :\n', sess.run(output_max_pool)
print '\n\ntf.nn.max_pool_with_argmax :\n', sess.run(output_max_pool_with_argmax)value :
[[[[ 1.]
[ 2.]
[ 3.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 7.]
[ 8.]
[ 9.]]]]
----------
tf.nn.avg_pool :
[[[[ 3.]]]]
tf.nn.max_pool :
[[[[ 5.]]]]
tf.nn.max_pool_with_argmax :
MaxPoolWithArgmax(output=array([[[[ 5.]]]], dtype=float32), argmax=array([[[[4]]]]))Morphological filtering
Normalization
tf.nn.l2_normalize
计算 output = x / sqrt(max(sum(x**2), epsilon)) 。
tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)
tf.nn.batch_normalization
Batch normalization。跟 tf.contrib.layers.batch_norm 的具体区别还没搞懂。貌似是同一个东西用不同 api 来实现。
tf.nn.batch_norm_with_global_normalization
Losses
tf.nn.l2_loss
tf.nn.log_poisson_loss
Classification
tf.nn.softmax
对 -1维 进行重新赋值，之后每个元素取值在 (0, 1) 范围内， 且总和为 1 。
计算公式：softmax = exp(logits) / reduce_sum(exp(logits), dim) 。
tf.nn.softmax (logits, dim=-1, name=None)
tf.nn.log_softmax
计算 log_softmax 。
计算公式：logsoftmax = logits - log(reduce_sum(exp(logits), dim)) 。
tf.nn.softmax (logits, dim=-1, name=None)
# coding=utf-8
import tensorflow as tf
x = tf.placeholder(dtype=tf.float32)
# 保证每次都是 同一组features
import numpy as np
i_p = np.random.uniform(-1, 1, [5])
# tensorflow 自带的 计算接口
output_softmax = tf.nn.softmax(x)
output_log_softmax = tf.nn.log_softmax(x)
# 自己定义的 softmax、log_softmax 计算函数：
diy_softmax = lambda logits: tf.divide(tf.exp(logits), tf.reduce_sum(tf.exp(logits), -1))
diy_log_softmax = lambda logits: tf.subtract(logits, tf.log(tf.reduce_sum(tf.exp(logits), -1)))
with tf.Session() as sess:
print '\ninput :\n', sess.run(x, feed_dict={x:i_p})
print '\n----------\n'
print '\ntf.nn.softmax :\n', sess.run(output_softmax, feed_dict={x:i_p})
print '\ndiy_softmax :\n', sess.run(diy_softmax(x), feed_dict={x:i_p})
print '\ntf.nn.log_softmax :\n', sess.run(output_log_softmax, feed_dict={x:i_p})
print '\ndiy_log_softmax :\n', sess.run(diy_log_softmax(x), feed_dict={x:i_p})根据打印结果可见，自己 根据数学公式 定义的 softmax 函数 、 log_softmax 函数 得到了和 tf.nn.softmax() 、 tf.nn.log_softmax() 接口一样的计算结果：
input :
[-0.33777472 0.34060675 0.70219433 -0.98343688 0.405606 ]
----------
tf.nn.softmax :
[ 0.11866388 0.23384923 0.33571553 0.06221728 0.24955413]
diy_softmax :
[ 0.11866388 0.23384921 0.33571553 0.06221728 0.2495541 ]
tf.nn.log_softmax :
[-2.13146019 -1.45307875 -1.0914911 -2.7771225 -1.3880794 ]
diy_log_softmax :
[-2.13146019 -1.45307875 -1.09149122 -2.7771225 -1.38807952]Embeddings
tf.nn.embedding_lookup
tf.nn.embedding_lookup_sparse
Recurrent Neural Networks
Connectionist Temporal Classification (CTC)
Evaluation
tf.nn.top_k
tf.nn.in_top_k
Candidate Sampling
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原始发表：2017年10月03日，如有侵权请联系 cloudcommunity@tencent.com 删除
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