我使用以下代码合并了CNN和LSTM：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
import pyfftw
from scipy import signal
import xlrd
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib
import seaborn as sns
from sklearn.metrics import confusion_matrix
##matplotlib inline
plt.style.use('ggplot')
## define funtions
def read_data(file_path):
## column_names = ['user-id','activity','timestamp', 'x-axis', 'y-axis', 'z-axis']
column_names = ['activity','timestamp', 'Ax', 'Ay', 'Az', 'Lx', 'Ly', 'Lz', 'Gx', 'Gy', 'Gz', 'Mx', 'My', 'Mz'] ## 3 sensors
data = pd.read_csv(file_path,header = None, names = column_names)
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['Ax'], 'x-axis')
plot_axis(ax1, data['timestamp'], data['Ay'], 'y-axis')
plot_axis(ax2, data['timestamp'], data['Az'], 'z-axis')
plt.subplots_adjust(hspace=0.2)
fig.suptitle(activity)
plt.subplots_adjust(top=0.90)
plt.show()
def windows(data, size):
start = 0
while start < data.count():
yield start, start + size
start += (size / 2)
def segment_signal(data, window_size = None, num_channels=None): # edited
segments = np.empty((0,window_size,num_channels)) #change from 3 to 9 channels for AGM fusion #use variable num_channels=9
labels = np.empty((0))
for (n_start, n_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]
n_start = int(n_start)
n_end = int(n_end)
Ax = data["Ax"][n_start:n_end]
Ay = data["Ay"][n_start:n_end]
Az = data["Az"][n_start:n_end]
Lx = data["Lx"][n_start:n_end]
Ly = data["Ly"][n_start:n_end]
Lz = data["Lz"][n_start:n_end]
Gx = data["Gx"][n_start:n_end]
Gy = data["Gy"][n_start:n_end]
Gz = data["Gz"][n_start:n_end]
Mx = data["Mx"][n_start:n_end]
My = data["My"][n_start:n_end]
Mz = data["Mz"][n_start:n_end]
if(len(data['timestamp'][n_start:n_end]) == window_size): # include only windows with size of 90
segments = np.vstack([segments,np.dstack([Ax,Ay,Az,Gx,Gy,Gz,Mx,My,Mz])])
labels = np.append(labels,stats.mode(data["activity"][n_start:n_end])[0][0])
return segments, labels
def weight_variable(shape, restore_name):
initial = tf.truncated_normal(shape, stddev = 0.1)
return tf.Variable(initial, name=restore_name)
def bias_variable(shape, restore_name):
initial = tf.constant(0.0, shape = shape)
return tf.Variable(initial, name=restore_name)
def depthwise_conv2d(x, W):
return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')
def apply_depthwise_conv(x,weights,biases):
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')
#------------------------get dataset----------------------#
## run shoaib_dataset.py to generate dataset_shoaib_total.txt
## get data from dataset_shoaib_total.txt
dataset_belt = read_data('dataset_shoaibsensoractivity_participant_belt.txt')
dataset_left_pocket = read_data('dataset_shoaibsensoractivity_participant_left_pocket.txt')
dataset_right_pocket = read_data('dataset_shoaibsensoractivity_participant_right_pocket.txt')
dataset_upper_arm = read_data('dataset_shoaibsensoractivity_participant_upper_arm.txt')
dataset_wrist = read_data('dataset_shoaibsensoractivity_participant_wrist.txt')
#--------------------preprocessing------------------------#
dataset_belt['Ax'] = feature_normalize(dataset_belt['Ax'])
dataset_belt['Ay'] = feature_normalize(dataset_belt['Ay'])
dataset_belt['Az'] = feature_normalize(dataset_belt['Az'])
dataset_belt['Gx'] = feature_normalize(dataset_belt['Gx'])
dataset_belt['Gy'] = feature_normalize(dataset_belt['Gy'])
dataset_belt['Gz'] = feature_normalize(dataset_belt['Gz'])
dataset_belt['Mx'] = feature_normalize(dataset_belt['Mx'])
dataset_belt['My'] = feature_normalize(dataset_belt['My'])
dataset_belt['Mz'] = feature_normalize(dataset_belt['Mz'])
dataset_left_pocket['Ax'] = feature_normalize(dataset_left_pocket['Ax'])
dataset_left_pocket['Ay'] = feature_normalize(dataset_left_pocket['Ay'])
dataset_left_pocket['Az'] = feature_normalize(dataset_left_pocket['Az'])
dataset_left_pocket['Gx'] = feature_normalize(dataset_left_pocket['Gx'])
dataset_left_pocket['Gy'] = feature_normalize(dataset_left_pocket['Gy'])
dataset_left_pocket['Gz'] = feature_normalize(dataset_left_pocket['Gz'])
dataset_left_pocket['Mx'] = feature_normalize(dataset_left_pocket['Mx'])
dataset_left_pocket['My'] = feature_normalize(dataset_left_pocket['My'])
dataset_left_pocket['Mz'] = feature_normalize(dataset_left_pocket['Mz'])
dataset_right_pocket['Ax'] = feature_normalize(dataset_right_pocket['Ax'])
dataset_right_pocket['Ay'] = feature_normalize(dataset_right_pocket['Ay'])
dataset_right_pocket['Az'] = feature_normalize(dataset_right_pocket['Az'])
dataset_right_pocket['Gx'] = feature_normalize(dataset_right_pocket['Gx'])
dataset_right_pocket['Gy'] = feature_normalize(dataset_right_pocket['Gy'])
dataset_right_pocket['Gz'] = feature_normalize(dataset_right_pocket['Gz'])
dataset_right_pocket['Mx'] = feature_normalize(dataset_right_pocket['Mx'])
dataset_right_pocket['My'] = feature_normalize(dataset_right_pocket['My'])
dataset_right_pocket['Mz'] = feature_normalize(dataset_right_pocket['Mz'])
dataset_upper_arm['Ax'] = feature_normalize(dataset_upper_arm['Ax'])
dataset_upper_arm['Ay'] = feature_normalize(dataset_upper_arm['Ay'])
dataset_upper_arm['Az'] = feature_normalize(dataset_upper_arm['Az'])
dataset_upper_arm['Gx'] = feature_normalize(dataset_upper_arm['Gx'])
dataset_upper_arm['Gy'] = feature_normalize(dataset_upper_arm['Gy'])
dataset_upper_arm['Gz'] = feature_normalize(dataset_upper_arm['Gz'])
dataset_upper_arm['Mx'] = feature_normalize(dataset_upper_arm['Mx'])
dataset_upper_arm['My'] = feature_normalize(dataset_upper_arm['My'])
dataset_upper_arm['Mz'] = feature_normalize(dataset_upper_arm['Mz'])
dataset_wrist['Ax'] = feature_normalize(dataset_wrist['Ax'])
dataset_wrist['Ay'] = feature_normalize(dataset_wrist['Ay'])
dataset_wrist['Az'] = feature_normalize(dataset_wrist['Az'])
dataset_wrist['Gx'] = feature_normalize(dataset_wrist['Gx'])
dataset_wrist['Gy'] = feature_normalize(dataset_wrist['Gy'])
dataset_wrist['Gz'] = feature_normalize(dataset_wrist['Gz'])
dataset_wrist['Mx'] = feature_normalize(dataset_wrist['Mx'])
dataset_wrist['My'] = feature_normalize(dataset_wrist['My'])
dataset_wrist['Mz'] = feature_normalize(dataset_wrist['Mz'])
#------------------fixed hyperparameters--------------------#
window_size = 200 #from 90 #FIXED at 4 seconds
#----------------input hyperparameters------------------#
input_height = 1
input_width = window_size
num_labels = 7
num_channels = 9 #from 3 channels #9 channels for AGM
#-------------------sliding time window----------------#
segments_belt, labels_belt = segment_signal(dataset_belt, window_size=window_size, num_channels=num_channels)
labels_belt = np.asarray(pd.get_dummies(labels_belt), dtype = np.int8)
reshaped_segments_belt = segments_belt.reshape(len(segments_belt), (window_size*num_channels)) #use variable num_channels instead of constant 3 channels
segments_left_pocket, labels_left_pocket = segment_signal(dataset_left_pocket, window_size=window_size, num_channels=num_channels)
labels_left_pocket = np.asarray(pd.get_dummies(labels_left_pocket), dtype = np.int8)
reshaped_segments_left_pocket = segments_left_pocket.reshape(len(segments_left_pocket), (window_size*num_channels)) #use variable num_channels instead of constant 3 channels
segments_right_pocket, labels_right_pocket = segment_signal(dataset_right_pocket, window_size=window_size, num_channels=num_channels)
labels_right_pocket = np.asarray(pd.get_dummies(labels_right_pocket), dtype = np.int8)
reshaped_segments_right_pocket = segments_right_pocket.reshape(len(segments_right_pocket), (window_size*num_channels)) #use variable num_channels instead of constant 3 channels
segments_upper_arm, labels_upper_arm = segment_signal(dataset_upper_arm, window_size=window_size, num_channels=num_channels)
labels_upper_arm = np.asarray(pd.get_dummies(labels_upper_arm), dtype = np.int8)
reshaped_segments_upper_arm = segments_upper_arm.reshape(len(segments_upper_arm), (window_size*num_channels)) #use variable num_channels instead of constant 3 channels
segments_wrist, labels_wrist = segment_signal(dataset_wrist, window_size=window_size, num_channels=num_channels)
labels_wrist = np.asarray(pd.get_dummies(labels_wrist), dtype = np.int8)
reshaped_segments_wrist = segments_wrist.reshape(len(segments_wrist), (window_size*num_channels)) #use variable num_channels instead of constant 3 channels
##reshaped_segments = np.vstack([reshaped_segments1,reshaped_segments2,reshaped_segments3,reshaped_segments4,reshaped_segments5,reshaped_segments6,reshaped_segments7,reshaped_segments8,reshaped_segments9,reshaped_segments10])
##labels = np.vstack([labels1,labels2,labels3,labels4,labels5,labels6,labels7,labels8,labels9,labels10])
# all locations
reshaped_segments = np.vstack([reshaped_segments_belt,reshaped_segments_left_pocket,reshaped_segments_right_pocket,reshaped_segments_upper_arm,reshaped_segments_wrist])
labels = np.vstack([labels_belt,labels_left_pocket,labels_right_pocket,labels_upper_arm,labels_wrist])
#------------divide data into test and training `set-----------#
train_test_split = np.random.rand(len(reshaped_segments)) < 0.70
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]
#---------------training hyperparameters----------------#
batch_size = 10
kernel_size = 60 #from 60 #optimal 2
depth = 15 #from 60 #optimal 15
num_hidden = 1000 #from 1000 #optimal 80
learning_rate = 0.0001
training_epochs = 8
total_batches = train_x.shape[0] ##// batch_size # included // batch_size
#---------define placeholders for input----------#
X = tf.placeholder(tf.float32, shape=[None,input_width * num_channels], name="input")
X_reshaped = tf.reshape(X,[-1,input_height,input_width,num_channels])
Y = tf.placeholder(tf.float32, shape=[None,num_labels])
#---------------------perform convolution-----------------#
# first convolutional layer
c_weights = weight_variable([1, kernel_size, num_channels, depth], restore_name="c_weights")
c_biases = bias_variable([depth * num_channels], restore_name="c_biases")
c = apply_depthwise_conv(X_reshaped,c_weights,c_biases)
p = apply_max_pool(c,20,2)
# second convolutional layer
c2_weights = weight_variable([1, 6,depth*num_channels,depth//10], restore_name="c2_weights")
c2_biases = bias_variable([(depth*num_channels)*(depth//10)], restore_name="c2_biases")
c2 = apply_depthwise_conv(p,c2_weights,c2_biases)
n_classes = 7
n_hidden = 128
n_inputs = 540 # 540 = 60*3 not 180 # or 7*9*10
lstm_size = 128
rnnW = {
'hidden': tf.Variable(tf.random_normal([n_inputs, n_hidden])),
'output': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
rnnBiases = {
'hidden': tf.Variable(tf.random_normal([n_hidden], mean=1.0)),
'output': tf.Variable(tf.random_normal([n_classes]))
}
c2Reshape = tf.reshape(c2, [-1, 7, 200])
shuff = tf.transpose(c2Reshape, [1, 0, 2])
shuff = tf.reshape(shuff, [-1, n_inputs])
# Linear activation, reshaping inputs to the LSTM's number of hidden:
hidden = tf.nn.relu(tf.matmul(
shuff, rnnW['hidden']
) + rnnBiases['hidden'])
# Split the series because the rnn cell needs time_steps features, each of shape:
hidden = tf.split(axis=0, num_or_size_splits=7, value=hidden)
lstm_cell = tf.contrib.rnn.LSTMCell(lstm_size, forget_bias=1.0)
# Stack two LSTM layers, both layers has the same shape
lstm_layers = tf.contrib.rnn.MultiRNNCell([lstm_cell] * 2)
lstmOutputs, _ = tf.contrib.rnn.static_rnn(lstm_layers, hidden, dtype=tf.float32)
lstmLastOutput = lstmOutputs[-1]
y_ = tf.matmul(lstmLastOutput, rnnW['output']) + rnnBiases['output']
#-----------------loss optimization-------------#
loss = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)
##optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss)
#-----------------compute accuracy---------------#
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
cost_history = np.empty(shape=[1],dtype=float)
saver = tf.train.Saver()
#-----------------run session--------------------#
session = tf.Session()
session.run(tf.global_variables_initializer())
for epoch in range(8):
for b in range(total_batches):
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}))
if 1==1:
print ("Testing Accuracy: ", session.run(accuracy, feed_dict={X: test_x, Y: test_y}),'\n')
pred_y = session.run(tf.argmax(y_ ,1),feed_dict={X: test_x})
cm = confusion_matrix(np.argmax(test_y ,1),pred_y)
print (cm, '\n')
plt.imshow(cm)
plt.title('Confusion Matrix')
plt.rcParams['image.cmap'] = 'afmhot'
plt.colorbar()
tick_marks = np.arange(len(['Wal', 'Std', 'Jog', 'Sit', 'Bik', 'Wlu', 'Wld']))
plt.xticks(tick_marks, ['Wal', 'Std', 'Jog', 'Sit', 'Bik', 'Wlu', 'Wld'])
plt.yticks(tick_marks, ['Wal', 'Std', 'Jog', 'Sit', 'Bik', 'Wlu', 'Wld'])
fmt = '.2f'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.figure()
plt.show()
但是，我总是得到这个错误：
Traceback（最近一次调用最后一次）：文件 ＆＃34; C：\用户\夏琳\应用程序数据\本地\程序\的Python \ Python35 \ lib中\站点包\ tensorflow \蟒\客户\ session.py＆＃34 ;, 第1322行，在_do_call中 return fn（* args）File＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ client \ session.py＆＃34;， 第1307行，在_run_fn中 options，feed_dict，fetch_list，target_list，run_metadata）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ client \ session.py＆＃34; ， 第1409行，在_call_tf_sessionrun中 run_metadata）tensorflow.python.framework.errors_impl.InvalidArgumentError： 形状不相容：[10,7]与[20,7] [[节点：mul = Mul [T = DT_FLOAT， _device =＆＃34; / job：localhost / replica：0 / task：0 / device：CPU：0＆＃34;]（_ arg_Placeholder_0_0，Log）]]
在处理上述异常期间，发生了另一个异常：
回溯（最近一次呼叫最后）：文件＆＃34;＆＃34;，第6行，in _，c = session.run（[optimizer，loss]，feed_dict = {X：batch_x，Y：batch_y}）文件 ＆＃34; C：\用户\夏琳\应用程序数据\本地\程序\的Python \ Python35 \ lib中\站点包\ tensorflow \蟒\客户\ session.py＆＃34 ;, 第900行，在运行中 run_metadata_ptr）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ client \ session.py＆＃34;， 第1135行，在_run feed_dict_tensor，options，run_metadata）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ client \ session.py＆＃34;， 第1316行，在_do_run中 run_metadata）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ client \ session.py＆＃34;， 第1335行，在_do_call中 raise type（e）（node_def，op，message）tensorflow.python.framework.errors_impl.InvalidArgumentError： 形状不相容：[10,7]与[20,7] [[节点：mul = Mul [T = DT_FLOAT， _device =＆＃34; / job：localhost / replica：0 / task：0 / device：CPU：0＆＃34;]（_ arg_Placeholder_0_0，Log）]]
由op＆＃39; mul＆＃39;引起，定义于：File＆＃34;＆＃34;，第1行，in 文件 ＆＃34; C：\用户\夏琳\应用程序数据\本地\程序\的Python \ Python35 \ lib中\ idlelib \ run.py＆＃34 ;, 第130行，主要 ret = method（* args，** kwargs）File＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ idlelib \ run.py＆＃34;， 第357行，在runcode中 exec（code，self.locals）File＆＃34;＆＃34;，第2行，在File中 ＆＃34; C：\用户\夏琳\应用程序数据\本地\程序\的Python \ Python35 \ lib中\站点包\ tensorflow \蟒\ OPS \ math_ops.py＆＃34 ;, 第979行，在binary_op_wrapper中 return func（x，y，name = name）File＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ ops \ math_ops.py＆＃34 ;， 第1211行，在_mul_dispatch中 return gen_math_ops.mul（x，y，name = name）File＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ ops \ gen_math_ops.py＆ ＃34 ;, 第5066行，在mul ＆＃34; Mul＆＃34;，x = x，y = y，name = name）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \蟒\框架\ op_def_library.py＆＃34 ;, 第787行，在_apply_op_helper中 op_def = op_def）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ framework \ ops.py＆＃34;， 第3392行，在create_op中 op_def = op_def）文件＆＃34; C：\ Users \ Charlene \ AppData \ Local \ Programs \ Python \ Python35 \ lib \ site-packages \ tensorflow \ python \ framework \ ops.py＆＃34;， 第1718行，在 init 中 self._traceback = self._graph._extract_stack（）#pylint：disable = protected-access
InvalidArgumentError（参见上面的回溯）：不兼容的形状： [10,7] vs. [20,7] [[Node：mul = Mul [T = DT_FLOAT， _device =＆＃34; / job：localhost / replica：0 / task：0 / device：CPU：0＆＃34;]（_ arg_Placeholder_0_0，Log）]]
我看到的主要错误是：
不相容的形状：[10,7]与[20,7]
其中10是批量大小，7是类的数量。
什么是错误？
答案 0 :(得分：0)
似乎错误发生在这里：
loss = -tf.reduce_sum(Y * tf.log(y_))
Y (10, 7)是预期的，但y_出于某种原因(20, 7)。
尝试在此行之前跟踪c2的形状：
c2Reshape = tf.reshape(c2, [-1, 7, 200])
它后面的c2Reshape的形状（或者只是暂时替换c2Reshape = tf.reshape(c2, [10, 7, 200])并查看它是否失败），我怀疑这是20来自哪里。