一维时间卷积tensorflow代码
时间: 2023-10-23 17:10:18 浏览: 29
import tensorflow as tf
# 定义输入数据
inputs = tf.placeholder(shape=[None, 10, 1], dtype=tf.float32)
# 定义卷积核
filter_size = 3
num_filters = 32
filter_shape = [filter_size, 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
# 定义卷积层
conv = tf.nn.conv1d(inputs, W, stride=1, padding="SAME")
# 定义激活函数
bias = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
h = tf.nn.relu(tf.nn.bias_add(conv, bias), name="relu")
# 定义池化层
pool_size = 2
pool = tf.nn.max_pool(h, ksize=[1, pool_size, 1, 1], strides=[1, pool_size, 1, 1], padding="SAME")
# 定义全连接层
fc_size = 128
pool_shape = pool.get_shape().as_list()
pool_flat = tf.reshape(pool, [-1, pool_shape[1] * pool_shape[2]])
W_fc = tf.Variable(tf.truncated_normal([pool_flat.get_shape()[1], fc_size], stddev=0.1), name="W_fc")
b_fc = tf.Variable(tf.constant(0.1, shape=[fc_size]), name="b_fc")
fc = tf.nn.relu(tf.matmul(pool_flat, W_fc) + b_fc)
# 定义输出层
num_classes = 10
W_out = tf.Variable(tf.truncated_normal([fc_size, num_classes], stddev=0.1), name="W_out")
b_out = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b_out")
logits = tf.matmul(fc, W_out) + b_out
# 定义损失函数和优化器
labels = tf.placeholder(shape=[None, num_classes], dtype=tf.float32)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cross_entropy)
# 定义准确率
correct_predictions = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
# 训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(num_iterations):
batch_inputs, batch_labels = ...
sess.run(optimizer, feed_dict={inputs: batch_inputs, labels: batch_labels})
if i % 100 == 0:
train_loss, train_acc = sess.run([cross_entropy, accuracy], feed_dict={inputs: batch_inputs, labels: batch_labels})
print("Iteration {}, Train Loss {:.3f}, Train Acc {:.3f}".format(i, train_loss, train_acc))