tf.nn.conv2d
import numpy as np
filter_primes = np.array([2., 3., 5., 7., 11., 13.], dtype=np.float32)
x = tf.constant(np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))
filters = tf.constant(filter_primes.reshape(1, 6, 1, 1))
valid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')
same_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')
with tf.Session() as sess:
print("VALID:\n", valid_conv.eval())
print("SAME:\n", same_conv.eval())
import numpy as np
filter_primes = np.array([2., 3., 5., 7., 11., 13.], dtype=np.float32)
x = tf.constant(np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))#<class 'tensorflow.python.framework.ops.Tensor'>
filters = tf.constant(filter_primes.reshape(1, 6, 1, 1)) #<class 'tensorflow.python.framework.ops.Tensor'>
print(type(x))
print(type(filters))
valid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')
same_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')
with tf.Session() as sess:
print("VALID:\n", valid_conv.eval())
print("SAME:\n", same_conv.eval())
import tensorflow as tf
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
c = a + b
with tf.Session() as sess:
print(c.eval(feed_dict={a:1.0,b:3.3}))
posted on 2019-05-29 17:36 happygril3 阅读(143) 评论(0) 编辑 收藏 举报