我正在使用Tensorflow训练我的数据以进行手势识别。这是我的代码：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
%matplotlib inline
plt.style.use('ggplot')
def read_data(file_path):
column_names = ['user-id','activity','timestamp','x-axis','y-axis','z-axis']
data = pd.read_csv(file_path,header = None, names = column_names, comment=';')
return data
def feature_normalize(dataset):
mu = np.mean(dataset,axis = 0)
sigma = np.std(dataset,axis = 0)
return (dataset - mu)/sigma
def plot_axis(ax, x, y, title):
ax.plot(x, y)
ax.set_title(title)
ax.xaxis.set_visible(False)
ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])
ax.set_xlim([min(x), max(x)])
ax.grid(True)
def plot_activity(activity,data):
fig, (ax0, ax1, ax2) = plt.subplots(nrows = 3, figsize = (15, 10), sharex = True)
plot_axis(ax0, data['timestamp'], data['x-axis'], 'x-axis')
plot_axis(ax1, data['timestamp'], data['y-axis'], 'y-axis')
plot_axis(ax2, data['timestamp'], data['z-axis'], 'z-axis')
plt.subplots_adjust(hspace=0.2)
fig.suptitle(activity)
plt.subplots_adjust(top=0.90)
plt.show()
dataset = read_data('C:\\Users\\ASUS\\Desktop\\final_data.txt')
dataset['x-axis'] = feature_normalize(dataset['x-axis'])
dataset['y-axis'] = feature_normalize(dataset['y-axis'])
dataset['z-axis'] = feature_normalize(dataset['z-axis'])
for activity in np.unique(dataset["activity"]):
subset = dataset[dataset["activity"] == activity][:34]
plot_activity(activity,subset)
def windows(data, size):
start = 0
while start < data.count():
yield int(start), int(start + size)
start += (size / 2)
def segment_signal(data,window_size = 17):
segments = np.empty((0,window_size,3))
labels = np.empty((0))
for (start, end) in windows(data["timestamp"], window_size):
x = data["x-axis"][start:end]
y = data["y-axis"][start:end]
z = data["z-axis"][start:end]
if(len(dataset["timestamp"][start:end]) == window_size):
segments = np.vstack([segments,np.dstack([x,y,z])])
labels = np.append(labels,stats.mode(data["activity"][start:end])[0][0])
return segments, labels
segments, labels = segment_signal(dataset)
labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)
reshaped_segments = segments.reshape(len(segments), 1,17, 3)
train_test_split = np.random.rand(len(reshaped_segments)) < 0.80
train_x = reshaped_segments[train_test_split]
train_y = labels[train_test_split]
test_x = reshaped_segments[~train_test_split]
test_y = labels[~train_test_split]
input_height = 1
input_width = 17
num_labels = 7
num_channels = 3
batch_size = 100
kernel_size = 5
depth = 60
num_hidden = 1000
learning_rate = 0.0001
training_epochs = 10
total_batchs = train_x.shape[0] // batch_size
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev = 0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.0, shape = shape)
return tf.Variable(initial)
def depthwise_conv2d(x, W):
return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')
def apply_depthwise_conv(x,kernel_size,num_channels,depth):
weights = weight_variable([1, kernel_size, num_channels, depth])
biases = bias_variable([depth * num_channels])
return tf.nn.relu(tf.add(depthwise_conv2d(x, weights),biases))
def apply_max_pool(x,kernel_size,stride_size):
return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1],
strides=[1, 1, stride_size, 1], padding='VALID')
X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels],name="Mul")
Y = tf.placeholder(tf.float32, shape=[None,num_labels])
c = apply_depthwise_conv(X,kernel_size,num_channels,depth)
p = apply_max_pool(c,4,2)
c = apply_depthwise_conv(p,3,depth*num_channels,depth//6)
shape = c.get_shape().as_list()
c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])
f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//6), num_hidden]) #10
f_biases_l1 = bias_variable([num_hidden])
f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))
out_weights = weight_variable([num_hidden, num_labels])
out_biases = bias_variable([num_labels])
y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases, name="y_")
loss = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
with tf.Session() as session:
tf.global_variables_initializer().run()
for epoch in range(training_epochs):
cost_history = np.empty(shape=[1],dtype=float)
for b in range(total_batchs):
offset = (b * batch_size) % (train_y.shape[0] - batch_size)
batch_x = train_x[offset:(offset + batch_size), :, :, :]
batch_y = train_y[offset:(offset + batch_size), :]
_, c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})
cost_history = np.append(cost_history,c)
print ("Epoch: ",epoch," Training Loss: ",c," Training Accuracy: ",
session.run(accuracy, feed_dict={X: train_x, Y: train_y}))
print ("Testing Accuracy:", session.run(accuracy, feed_dict={X: test_x, Y: test_y}))
tf.train.write_graph(session.graph_def, '.', '../har.pbtxt')
saver.save(session,save_path = "../har.ckpt")
在最后一行遇到此错误消息
ValueError：无法为张量为'（？，7）'的张量'Placeholder_2：0'输入形状（10，9）的值
我尝试更改一些值，但仍然无法解决此错误。
我该怎么办才能解决此错误？