# dataset_fft = fft_transformer(dataset_dense, 100)
# dataset = np.hstack((dataset_4feature, dataset_fft, label))
# dataset_guiyi_1 = guiyi(dataset)
# print(dataset_guiyi_1.shape)
# print(Counter(dataset_guiyi_1[:, -1]))
# print('onehot编码后结果：')
# one_hot = onehot(dataset_guiyi_1)
# print(one_hot.shape)
# print(np.sum(one_hot[:, -3:], axis=0))
# print(np.min(dataset_guiyi_1, axis=0))
# SaveFile(data=dataset_guiyi_1, savepickle_p=p1)
data_all2 = making2(p=p, argchoice_r=argchoice_r2)
dataset_4feature, dataset_dense, label = data_all2[:, :
4], data_all2[:, 4:
-1], data_all2[:,
-1][:,
np
.
newaxis]
dataset_fft = fft_transformer(dataset_dense, 100)
dataset = np.hstack((dataset_4feature, dataset_fft, label))
dataset_guiyi_2 = guiyi(dataset)
print(dataset_guiyi_2.shape)
print('onehot编码后结果: ')
one_hot2 = onehot(dataset_guiyi_2)
print(one_hot2.shape)
print(np.sum(one_hot2[:, -11:], axis=0))
# print(np.min(dataset_guiyi_2, axis=0))
SaveFile(data=dataset_guiyi_2, savepickle_p=p2)
def session(dataset_path, train_path='', test_path=''):
'''
节点连接
:param dataset_path: 数据集路径
:param train_path: 训练集数据路径,默认为空
:param test_path: 测试集数据路径,默认为空
:return: None
'''
#导入数据集
dataset = LoadFile(p=dataset_path)
# dataset = guiyi(dataset)
dataset = onehot(dataset)
g = tf.Graph()
with g.as_default():
with tf.name_scope('placeholder'):
x_f = tf.placeholder(dtype=tf.float32, shape=[None, 4], name='x_f')
x_l = tf.placeholder(dtype=tf.float32,
shape=[None, 100],
name='x_l')
y = tf.placeholder(dtype=tf.float32, shape=[None, 3], name='y')
learning_rate = tf.placeholder(dtype=tf.float32, name='lr')
is_training = tf.placeholder(dtype=tf.bool, name='is_training')
output = layers(x_f=x_f, x_l=x_l, is_training=is_training)
with tf.name_scope('prediction'):
# loss = -tf.reduce_mean(y * tf.log(output), name='loss')
loss = tf.reduce_mean(
tf.keras.losses.categorical_crossentropy(y_true=y,
y_pred=output))
opt = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).minimize(loss)
acc = tf.reduce_mean(tf.cast(
tf.equal(tf.keras.backend.argmax(output, axis=1),
tf.keras.backend.argmax(y, axis=1)), tf.float32),
name='pred')
with tf.name_scope('etc'):
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options),
graph=g) as sess:
sess.run(init)
#划分训练集和测试集
train_data, test_data = spliting(dataset, 3000)
for i in range(6000): #6000
for data in input(dataset=train_data, batch_size=500):
_ = sess.run(opt,
feed_dict={
x_f: data[:, :4],
x_l: data[:, 4:-3],
y: data[:, -3:],
learning_rate: 1e-2,
is_training: False
})
if i % 100 == 0:
loss_ = sess.run(loss,
feed_dict={
x_f: data[:, :4],
x_l: data[:, 4:-3],
y: data[:, -3:],
is_training: False
})
acc_1 = sess.run(acc,
feed_dict={
x_f: data[:, :4],
x_l: data[:, 4:-3],
y: data[:, -3:],
is_training: False
})
if i % 100 == 0:
acc_2 = sess.run(acc,
feed_dict={
x_f: test_data[:, :4],
x_l: test_data[:, 4:-3],
y: test_data[:, -3:],
is_training: False
})
print('第%s轮训练集损失函数值为: %s 训练集准确率为: %s 测试集准确率为: %s' %
(i, loss_, acc_1, acc_2))
tf.summary.FileWriter('log/first_graph', sess.graph)
return fft_abs
if __name__ == '__main__':
p = r'/home/xiaosong/桌面/OLDENBURG_all.pickle'
dataset = LoadFile(p)
nums_cl = [[6557, 0], [611, 2], [101, 2], [13, 2], [554, 2], [155, 2],
[100, 2], [1165, 1], [1993, 1], [947, 2], [1133, 2], [1152, 1],
[542, 2], [754, 2], [2163, 1]]
dataset_output = making(nums_cl=nums_cl, dataset=dataset)
print(dataset_output.shape)
checkclassifier(dataset_output[:, -1])
# SaveFile(dataset_output, savepickle_p=r'/home/xiaosong/桌面/OLDENBURG_3cl.pickle')
dataset_4feature, dataset_dense, label = dataset_output[:, :
4], dataset_output[:,
4:
-1], dataset_output[:,
-1][:,
np
.
newaxis]
dataset_fft = fft_transformer(dataset_dense, 100)
dataset = np.hstack((dataset_4feature, dataset_fft, label))
dataset_guiyi = guiyi(dataset)
print(dataset_guiyi.shape)
# print(np.min(dataset_guiyi, axis=0))
SaveFile(data=dataset_guiyi,
savepickle_p=r'/home/xiaosong/桌面/OLDENBURG_3cl.pickle')
dataset_onehot = onehot(dataset_guiyi)
print(np.sum(dataset_onehot[:, -3:], axis=0))