def evaluate(num):
# num 表示要取那个人的数据
# return 第num 个人数据经过测试得到数据。
with tf.name_scope("input"):
input_x = tf.placeholder(tf.float32, [bat[num], 500, 9, 1], name='EEG-input') # 数据的输入，第一维表示一个batch中样例的个数
input_y = tf.placeholder(tf.float32, [None, 7], name='EEG-lable') # 一个batch里的lable
regularlizer = tf.contrib.layers.l2_regularizer(Regularazition_Rate)#本来测试的时候不用加这个
is_training = tf.cast(False, tf.bool)
out = BaseCNN(input_x, is_training, regularlizer)
y = out['logit']
with tf.name_scope("test_acc"):
correct_predection = tf.equal(tf.argmax(y,1),tf.argmax(input_y,1))
accuracy = tf.reduce_mean(tf.cast(correct_predection,tf.float32))
tf.summary.scalar('test_acc', accuracy)
variable_averages = tf.train.ExponentialMovingAverage(Moving_Average_Decay)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(Model_Save_Path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
x, y = test_batch(num)#获取第x个人的数据
reshape_xs = np.reshape(x,(-1,500,9,1))
ys = one_hot(y)
conv1, pool1, conv2, pool2, conv3, pool3, conv4, pool4, acc_score =sess.run([out['conv1'], out['pool1'], out['conv2'], out['pool2'],
out['conv3'], out['pool3'], out['conv4'], out['pool4'],
accuracy],feed_dict={input_x: reshape_xs, input_y: ys})
print("Afer %s training step, test accuracy = %g" % (global_step,acc_score))
else :
print("No checkpoint file found")
return conv1, pool1, conv2, pool2, conv3, pool3, conv4, pool4
def evaluate(num, channel, name, fliter):
# num 表示要取那个人的数据，channel 表示对那个通道数据感兴趣，name表示是哪一层，fliter 表示神经网络中间层滤波器的数量
# return 第num 个人数据经过测试得到的在name层处理后的输出数据对应channel的值。
with tf.name_scope("input"):
input_x = tf.placeholder(tf.float32, [5424, 500, 9, 1],
name='EEG-input') # 数据的输入，第一维表示一个batch中样例的个数
input_y = tf.placeholder(tf.float32, [None, 7],
name='EEG-lable') # 一个batch里的lable
regularlizer = tf.contrib.layers.l2_regularizer(
Regularazition_Rate) #本来测试的时候不用加这个
out = BaseCNN(input_x, False, regularlizer)
y = out['logit']
with tf.name_scope("test_acc"):
correct_predection = tf.equal(tf.argmax(y, 1), tf.argmax(input_y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predection, tf.float32))
tf.summary.scalar('test_acc', accuracy)
variable_averages = tf.train.ExponentialMovingAverage(Moving_Average_Decay)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
raw_channel = []
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(Model_Save_Path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
x, y = test_batch(num) #获取第x个人的数据
# x = x[100:200]
# y = y[100:200]
# xs = standardize(x)
reshape_xs = np.reshape(x, (-1, 500, 9, 1))
ys = one_hot(y)
data, acc_score = sess.run([out['pool4'], accuracy],
feed_dict={
input_x: reshape_xs,
input_y: ys
})
#length = data[0]*data[1]
for i in range(fliter):
temp = data[:, :, channel, i]
raw = np.reshape(temp, [173568]) # 数据的总长度
raw_channel.append(raw)
print("Afer %s training step, test accuracy = %g" %
(global_step, acc_score))
print(np.shape(data))
else:
print("No checkpoint file found")
return raw_channel