I combined CNN and LSTM using this code:
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()
However, I always get this error:
Traceback (most recent call last): File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1322, in _do_call return fn(*args) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1307, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1409, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [10,7] vs. [20,7] [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Log)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "", line 6, in _, c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y}) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 900, in run run_metadata_ptr) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1135, in _run feed_dict_tensor, options, run_metadata) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run run_metadata) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [10,7] vs. [20,7] [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Log)]]
Caused by op 'mul', defined at: File "", line 1, in File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\idlelib\run.py", line 130, in main ret = method(*args, **kwargs) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\idlelib\run.py", line 357, in runcode exec(code, self.locals) File "", line 2, in File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\math_ops.py", line 979, in binary_op_wrapper return func(x, y, name=name) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1211, in _mul_dispatch return gen_math_ops.mul(x, y, name=name) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 5066, in mul "Mul", x=x, y=y, name=name) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op op_def=op_def) File "C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in init self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Incompatible shapes: [10,7] vs. [20,7] [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_0_0, Log)]]
The main error here I see is:
Incompatible shapes: [10,7] vs. [20,7]
where 10 is the batch size and 7 is the number of classes.
What is cuasing the error?
