Tensorflow中使用CNN和LSTM的占位符大小和类型错误

Question

我结合CNN和LSTM使用以下代码：

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，我总是会遇到以下错误：

回溯（最后一次调用）：文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1322行，在“do\u call return fn（*args）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python\Python35\lib\site packages\tensorflow\client\session.Python”中，第1307行，在“run\fn选项”中，feed\u dict、fetch\u list、target\u list、run\u metadata）文件“C:\Users\Charlene\AppData\Local\Programs\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1409行，位于调用\u tf\u sessionrun\u metadata）tensorflow中。python框架错误。InvalidArgumentError:不兼容的形状：[10,7]vs[20,7][[Node:mul=mul[T=DT\u FLOAT，\u device=“/job:localhost/replica:0/task:0/device:CPU:0”]（\u arg\u占位符\u 0\u 0，Log）]]

在处理上述异常期间，发生了另一个异常：

回溯（最近的最后一次调用）：文件“”，第6行，在uC=会话中。运行（[optimizer，loss]，feed_dict={X:batch_X，Y:batch_Y}）文件“C:\Users\Charlene\AppData\Local\Programs\Python35\lib\site packages\tensorflow\Python\client\session.py”，第900行，在运行元数据（run-run-run-metadata）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”中，第1135行，在第1316行“C:\Users\Charlene\AppData\Local\Programs\Python35\lib\site packages\tensorflow\Python\client\session.py”文件“C:\Users\Charlene\AppData\Local\Programs\Python35\lib\site packages\tensorflow\Python\client\session.py”文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”第13，在_do_call raise type（e）（节点定义、操作、消息）tensorflow中。python框架错误。InvalidArgumentError:不兼容的形状：[10,7]vs[20,7][[Node:mul=mul[T=DT\u FLOAT，\u device=“/job:localhost/replica:0/task:0/device:CPU:0”]（\u arg\u占位符\u 0\u 0，Log）]]

由op“mul”引起，定义于：文件“”，第1行，文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\idlelib\run.py”，第130行，主ret=method（*args，**kwargs）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\idlelib\run.py”，第357行，运行代码执行（代码，self.locals）文件“，第2行，文件中“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\ops\math\u ops。py”，第979行，在二进制\u op\u包装返回func（x，y，name=name）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\ops\math\u ops”中。py“，第1211行，在发送返回gen\u math\u ops.mul（x，y，name=name）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\ops\gen\u math\u ops”中。py”，第5066行，在mul“mul”中，x=x，y=y，name=name）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\framework\op\u def\u library”。py”，第787行，在文件“C:\Users\Charlene\AppData\Local\Programs\Python35\lib\site packages\tensorflow\Python\framework\ops”中。py”，第3392行，在create_op_def=op_def）文件“C:\Users\Charlene\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\framework\ops”中。py“，第1718行，在init self._traceback=self._graph._extract_stack（）35; pylint:disable=protected access中

InvalidArgumentError（回溯见上文）：不兼容的形状：[10,7]与[20,7][[Node:mul=mul[T=DT\u FLOAT，\u device=“/job:localhost/replica:0/task:0/device:CPU:0”]（\u arg\u占位符\u 0\u 0，Log）]]

我看到的主要错误是：

不兼容的形状：[10,7]与[20,7]

其中10是批量大小，7是类数。

什么是错误？

Answer 1

错误似乎发生在这里：

loss = -tf.reduce_sum(Y * tf.log(y_))

您的Y是（10,7），这是预期的，但由于某种原因Y是（20,7）。

尝试在这行之前跟踪c2的形状：

c2Reshape = tf.reshape(c2, [-1, 7, 200])

和它后面的c2Reshape的形状（或者只是暂时用c2Reshape=tf.reshape（c2，[10,7,200]）替换那一行，看看是否失败），我怀疑这就是20来自。