ValueError: The target structure is of type `<class 'NoneType'>` None However the in-CodePudding

I am trying to deploy classification model for fall and no fall using accelerometer data in raspberry pi 4. However something seems wrong with either tensorflow or numpy when reshaping them. Exact error that I am getting is ValueError: The target structure is of type <class 'NoneType'> None However the input structure is a sequence (<class 'list'>) of length 0. [] nest cannot guarantee that it is safe to map one to the other.

Deployment code can be found here.

import numpy as np
import pandas as pd
from tensorflow.keras.models import load_model


import board
import scipy.stats as stats
import adafruit_mpu6050
from math import atan2,degrees,pow
import warnings
warnings.filterwarnings("ignore")

import pandas as pd
import numpy as np

print("Inititating")

model_trained = load_model('model_cnn.h5')
print('\n',model_trained.summary())

i2c = board.I2C()
mpu = adafruit_mpu6050.MPU6050(i2c)
mpu_accelerometer_range = adafruit_mpu6050.Range.RANGE_4_G

data1 = []
i = 0
while(i<2000):
    
    
    i =1
    ax,ay,az = mpu.acceleration
    
    data1.append([ax,ay,az])
    df = pd.DataFrame(data1)
    #print('length of df',len(df))
    columns = ['x','y','z']
    df.columns = columns
    #print(df)
    Fs = 50
    frame_size = Fs*4
    hop_size = Fs*2
    
    frames = []
    N_FEATURES = 3
    
    for i in range(0,len(df)-frame_size,hop_size):
        x = df['x'].values[i:i frame_size]
        y = df['y'].values[i:i frame_size]
        z = df['z'].values[i:i frame_size]
        
        frames.append([x,y,z])
        
    #print('leeeee',len(frames))
    
    # converting frames to numpy array
    frames = np.asarray(frames).reshape(-1,frame_size,N_FEATURES)
    #print('frames',len(frames))

    k = int((len(df)-frame_size)/hop_size) 1
    print('type is',type(k))
    
    
    X_test = frames.reshape(k,200,3,1)
    y_pred = model_trained.predict_classes(X_test)
    print(y_pred)

I am using hopping window approach on cnn data having 3 columns x-axis acceleration, y-axis acceleration, z-axis acceleration. When I run below code outside of while loop, model gives output but that can not be used for real time.

X_test = frames.reshape(k,200,3,1)
y_pred = model_trained.predict_classes(X_test)
print(y_pred)

Training code

# Importing

import numpy as np
import pandas as pd
#import seaborn as sns
import tensorflow as tf
#import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization,MaxPooling2D
from tensorflow.keras.layers import Conv2D,MaxPool2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import load_model


physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
    tf.config.experimental.set_memory_growth(physical_devices[0], True)

import os
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "2"


df = pd.read_csv('fall_nofall_10_with_labels.csv')



df.columns = ['a','activity','time','realtime','x','y','z','gyro-x','gyro-y','gyro-z']





data = df.drop(['a','realtime','gyro-x','gyro-y','gyro-z'],axis=1)


print(data['activity'].value_counts())




# # sampling rate


Fs = 50



activities = data['activity'].value_counts().index






balanced_data = data.drop(['time'], axis = 1).copy()






balanced_data['activity'].value_counts()



from sklearn.preprocessing import LabelEncoder
label = LabelEncoder()
balanced_data['label'] = label.fit_transform(df['activity'])



### Frame Prepration
import scipy.stats as stats
Fs = 50
frame_size = Fs*4 #(4 seconds)
# 200x200x3 will be feeded in
hop_size = Fs*2 #(How much overlap) make advancement with 100 data samples



def get_frames(df,frame_size,hop_size):
    N_FEATURES = 3 # input feature is x,y and z
    frames = []
    labels = []

    for i in range(0,len(df) - frame_size,hop_size):
        x = df['x'].values[i:i frame_size] # 0 to 4 second then 1 to 5 seconds
        y = df['y'].values[i:i frame_size]
        z = df['z'].values[i:i frame_size]

        # activity which comes most number of time we'll be considering that
        label = stats.mode(df['label'][i:i frame_size])[0][0]
        # labels = label[0][0]

        frames.append([x,y,z])
        labels.append(label)

    # convert into numpy array
    frames = np.asarray(frames).reshape(-1,frame_size,N_FEATURES)
    labels = np.asarray(labels)

    return frames,labels



X,y = get_frames(balanced_data,frame_size,hop_size)



print(X.shape,y.shape)




X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)
X_train.shape, X_test.shape




X_train[0].shape, X_test[0].shape



X_train = X_train.reshape(39,200,3,1)
X_test = X_test.reshape(10,200,3,1)



X_train[0].shape,X_test[0].shape


# ## Creating CNN model



model = Sequential()
model.add(Conv2D(16,(2,2),activation='relu',input_shape=X_train[0].shape))
# model.add(MaxPooling2D(pool_size=(1,1)))
model.add(Dropout(0.1))


model.add(Conv2D(64,(2,2),activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Flatten())

model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2,activation='sigmoid'))





model.summary()



model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
history = model.fit(X_train, y_train, epochs = 10, validation_data= (X_test, y_test), verbose=1)

model.save("model_cnn.h5")




# load the model
model_trained = load_model('model_cnn.h5')
# summary of the model
print('\n',model_trained.summary())




from sklearn.metrics import confusion_matrix,classification_report
y_pred = model_trained.predict_classes(X_test)



print(y_pred)



print(classification_report(y_pred,y_test))




print(confusion_matrix(y_pred,y_test))

complete traceback:

Traceback (most recent call last):
  File "cnn_nofall_live.py", line 68, in <module>
    y_pred = model_trained.predict_classes(X_test)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/sequential.py", line 338, in predict_classes
    proba = self.predict(x, batch_size=batch_size, verbose=verbose)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 1013, in predict
    use_multiprocessing=use_multiprocessing)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 498, in predict
    workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 475, in _model_iteration
    total_epochs=1)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 187, in run_one_epoch
    aggregator.finalize()
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_utils.py", line 353, in finalize
    self.results = nest.pack_sequence_as(self._structure, self.results)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/util/nest.py", line 504, in pack_sequence_as
    return _pack_sequence_as(structure, flat_sequence, expand_composites)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/util/nest.py", line 453, in _pack_sequence_as
    len(flat_sequence), truncate(flat_sequence, 100)))
ValueError: The target structure is of type `<class 'NoneType'>`
  None
However the input structure is a sequence (<class 'list'>) of length 0.
  []
nest cannot guarantee that it is safe to map one to the other.

CodePudding user response：

I think you need to post the full traceback. It is difficult to debug/help without seeing that

But OOTH one possible issue is that you're using "i" for the while loop and the for loop. Maybe change the for loop index to "j" or something.

Try running this code snippet to see what I mean

i=0
while(i<10):
   i =1
   for i in range(100):
      print(f"for loop i is {i}")
   print(f"while loop i is {i}")

CodePudding user response：

btw, you trying for this?

data1 = []
i = 0
while(i<2000):
    i =1
    ax,ay,az = mpu.acceleration
    data1.append([ax,ay,az])
    df = pd.DataFrame(data1, columns=['x','y','z'])
    frame_size = 200
    hop_size = 100
    
frames = []
N_FEATURES = 3
    
for i in range(0,len(df)-frame_size,hop_size):
    x = df['x'].values[i:i frame_size]
    y = df['y'].values[i:i frame_size]
    z = df['z'].values[i:i frame_size]
    
    frames.append([x,y,z])

frames = np.array(frames)

k = int((len(df)-frame_size)/hop_size)

X_test = frames.reshape(k,200,3,1)