ValueError: Found input variables with inconsistent numbers of samples: [4162, 3] - machine-learning

Question

I am facing an error in plotting my confusion matrix. I am giving the test labels and my predicted label in confusion matrix function but it is giving me the value error having the problem in number of samples.
Shape of My data is below.
Trainig Data Shape (4162, 224, 224, 3)
Training Data Labels Shape (4162, 5)
Testing Data Shape (3921, 224, 224, 3)
Testing Data Labels Shape (3921, 5)
Predicted Label is a bit ugly because of only 2 epochs run, I just wanted to plot the confusion matrix first so thats why.
predictingimage = "D:/compCarsThesisData/data/image/78/3/2010/0ba8d018cdc994.jpg" #67/1698/2010/6805eb92ac6c70.jpg"
predictImageRead = mpg.imread(predictingimage)
resizingImage = cv2.cv2.resize(predictImageRead,(224,224))
reshapedFinalImage = np.expand_dims(resizingImage, axis=0)
npimage = np.asarray(reshapedFinalImage)
m = model.predict(npimage)
print(m)
[array([[0.02502811, 0.01959323, 0.6556284 , 0.26472655, 0.03502375]],
dtype=float32), array([[5.8234303e-04, 3.1917400e-04, 9.4957882e-01, 1.8873921e-02,
3.0645736e-02]], dtype=float32), array([[0.02581117, 0.04752538, 0.81816435, 0.04812173, 0.06037736]],
dtype=float32)]
cm = confusion_matrix(train_labels_Encode,m)
plt.imshow(cm)
plt.show()
ERROR
Traceback (most recent call last):
File "d:/ThesisWork/seriouswork/Inception_SVM_CompCarsGoogleNetArchitecture.py", line 299, in <module>
cm = confusion_matrix(train_labels_hotEncode,n)
File "C:\Users\zeele\Miniconda3\lib\site-packages\sklearn\metrics\classification.py", line 253, in confusion_matrix
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\zeele\Miniconda3\lib\site-packages\sklearn\metrics\classification.py", line 71, in _check_targets
check_consistent_length(y_true, y_pred)
File "C:\Users\zeele\Miniconda3\lib\site-packages\sklearn\utils\validation.py", line 235, in check_consistent_length
" samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [4162, 3]
Classifier Code:
X_train = np.load('D:/Inception_preprocessed_data_Labels_2004/Top5/TrainingData_Top5.npy')#('D:/ThesisWork/S_224_Training_data.npy')#training_images
X_test = np.load('D:/Inception_preprocessed_data_Labels_2004/Top5/TrainingLabels_Top5.npy')#('D:/ThesisWork/S_224_Training_labels.npy')#training_labels
y_train = np.load('D:/Inception_preprocessed_data_Labels_2004/Top5/TestingData_Top5.npy')#('D:/ThesisWork/S_224_Testing_data.npy')#testing_images
y_test = np.load('D:/Inception_preprocessed_data_Labels_2004/Top5/TestingLabels_Top5.npy')#('D:/ThesisWork/S_224_Testing_labels.npy')#testing_labels
print(X_test)
le = preprocessing.LabelEncoder()
le.fit(X_test)
transform_trainLabels = le.transform(X_test)
print(transform_trainLabels)
print(le.inverse_transform(transform_trainLabels))
train_labels_hotEncode = np_utils.to_categorical(transform_trainLabels,len(set(transform_trainLabels)))
shuffle(X_train)
shuffle(train_labels_hotEncode)
le2 = preprocessing.LabelEncoder()
le2.fit(y_test)
transform_testLabels = le2.transform(y_test)
test_labels_hotEncode = np_utils.to_categorical(transform_testLabels,len(set(transform_testLabels)))
print(test_labels_hotEncode.shape)
shuffle(y_train)
shuffle(test_labels_hotEncode)
# print(train_labels_hotEncode[3000])
# exit()
# X_train = np.asarray(X_train / 255.0)
# y_train = np.asarray(y_train / 255.0)
# print("X_Training" ,X_train.shape, X_train)
# print("X_TEST", X_test.shape)
# print("Y_train", y_train.shape)
# print("y_test", y_test.shape)
# exit()
# plt.imshow(X_train[1])
# print(X_test)
# plt.imshow(y_train[1])
# print(y_test)
# plt.show()
print("Trainig Data Shape",X_train.shape)
print("Training Data Labels Shape",train_labels_hotEncode.shape)
print("Testing Data Shape", y_train.shape)
print("Testing Data Labels Shape", test_labels_hotEncode.shape)
# X_train = np.array(X_train).astype(np.float32)
# y_train = np.array(y_train).astype(np.float32)
def inception_module(image,
filters_1x1,
filters_3x3_reduce,
filter_3x3,
filters_5x5_reduce,
filters_5x5,
filters_pool_proj,
name=None):
conv_1x1 = Conv2D(filters_1x1, (1,1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer= bias_init)(image)
conv_3x3 = Conv2D(filters_3x3_reduce, (1,1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer= bias_init)(image)
conv_3x3 = Conv2D(filter_3x3,(3,3), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer=bias_init)(conv_3x3)
conv_5x5 = Conv2D(filters_5x5_reduce,(1,1), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer= bias_init)(image)
conv_5x5 = Conv2D(filters_5x5, (3,3), padding='same', activation='relu',kernel_initializer=kernel_init, bias_initializer=bias_init)(conv_5x5)
pool_proj = MaxPool2D((3,3), strides=(1,1), padding='same')(image)
pool_proj = Conv2D(filters_pool_proj, (1,1), padding='same', activation='relu', kernel_initializer=kernel_init, bias_initializer= bias_init)(pool_proj)
output = concatenate([conv_1x1, conv_3x3, conv_5x5, pool_proj], axis=3, name=name)
return output
kernel_init = keras.initializers.glorot_uniform()
bias_init = keras.initializers.Constant(value=0.2)
# IMG_SIZE = 64
input_layer = Input(shape=(224,224,3))
image = Conv2D(64,(7,7),padding='same', strides=(2,2), activation='relu', name='conv_1_7x7/2', kernel_initializer=kernel_init, bias_initializer=bias_init)(input_layer)
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_1_3x3/2')(image)
image = Conv2D(64, (1,1), padding='same', strides=(1,1), activation='relu', name='conv_2a_3x3/1' )(image)
image = Conv2D(192, (3,3), padding='same', strides=(1,1), activation='relu', name='conv_2b_3x3/1')(image)
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_2_3x3/2')(image)
image = inception_module(image,
filters_1x1= 64,
filters_3x3_reduce= 96,
filter_3x3 = 128,
filters_5x5_reduce=16,
filters_5x5= 32,
filters_pool_proj=32,
name='inception_3a')
image = inception_module(image,
filters_1x1=128,
filters_3x3_reduce=128,
filter_3x3=192,
filters_5x5_reduce=32,
filters_5x5=96,
filters_pool_proj=64,
name='inception_3b')
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_3_3x3/2')(image)
image = inception_module(image,
filters_1x1=192,
filters_3x3_reduce=96,
filter_3x3=208,
filters_5x5_reduce=16,
filters_5x5=48,
filters_pool_proj=64,
name='inception_4a')
image1 = AveragePooling2D((5,5), strides=3)(image)
image1 = Conv2D(128, (1,1), padding='same', activation='relu')(image1)
image1 = Flatten()(image1)
image1 = Dense(1024, activation='relu')(image1)
image1 = Dropout(0.7)(image1)
image1 = Dense(5, activation='softmax', name='auxilliary_output_1')(image1)
image = inception_module(image,
filters_1x1 = 160,
filters_3x3_reduce= 112,
filter_3x3= 224,
filters_5x5_reduce= 24,
filters_5x5= 64,
filters_pool_proj=64,
name='inception_4b')
image = inception_module(image,
filters_1x1= 128,
filters_3x3_reduce = 128,
filter_3x3= 256,
filters_5x5_reduce= 24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4c')
image = inception_module(image,
filters_1x1=112,
filters_3x3_reduce=144,
filter_3x3= 288,
filters_5x5_reduce= 32,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4d')
image2 = AveragePooling2D((5,5), strides=3)(image)
image2 = Conv2D(128, (1,1), padding='same', activation='relu')(image2)
image2 = Flatten()(image2)
image2 = Dense(1024, activation='relu')(image2)
image2 = Dropout(0.7)(image2) #Changed from 0.7
image2 = Dense(5, activation='softmax', name='auxilliary_output_2')(image2)
image = inception_module(image,
filters_1x1=256,
filters_3x3_reduce=160,
filter_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name= 'inception_4e')
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_4_3x3/2')(image)
image = inception_module(image,
filters_1x1=256,
filters_3x3_reduce=160,
filter_3x3= 320,
filters_5x5_reduce=32,
filters_5x5= 128,
filters_pool_proj=128,
name='inception_5a')
image = inception_module(image,
filters_1x1=384,
filters_3x3_reduce=192,
filter_3x3=384,
filters_5x5_reduce=48,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5b')
image = GlobalAveragePooling2D(name='avg_pool_5_3x3/1')(image)
image = Dropout(0.7)(image)
image = Dense(5, activation='softmax', name='output')(image)
model = Model(input_layer, [image,image1,image2], name='inception_v1')
model.summary()
epochs = 2
initial_lrate = 0.001 # Changed From 0.01
def decay(epoch, steps=100):
initial_lrate = 0.01
drop = 0.96
epochs_drop = 8
lrate = initial_lrate * math.pow(drop,math.floor((1+epoch)/epochs_drop))#
return lrate
sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
# nadam = keras.optimizers.Nadam(lr= 0.002, beta_1=0.9, beta_2=0.999, epsilon=None)
# keras
lr_sc = LearningRateScheduler(decay)
# rms = keras.optimizers.RMSprop(lr = initial_lrate, rho=0.9, epsilon=1e-08, decay=0.0)
# ad = keras.optimizers.adam(lr=initial_lrate)
model.compile(loss=['categorical_crossentropy', 'categorical_crossentropy','categorical_crossentropy'],loss_weights=[1,0.3,0.3], optimizer='sgd', metrics=['accuracy'])
# loss = 'categorical_crossentropy', 'categorical_crossentropy','categorical_crossentropy'
history = model.fit(X_train, [train_labels_hotEncode,train_labels_hotEncode,train_labels_hotEncode], validation_split=0.3,shuffle=True,epochs=epochs, batch_size= 32, callbacks=[lr_sc]) # batch size changed from 256 or 64 to 16(y_train,[y_test,y_test,y_test])
# validation_data=(y_train,[test_labels_hotEncode,test_labels_hotEncode,test_labels_hotEncode]), validation_data= (X_train, [train_labels_hotEncode,train_labels_hotEncode,train_labels_hotEncode]),
print(history.history.keys())
plt.plot(history.history['output_acc'])
plt.plot(history.history['val_output_acc'])
plt.title('Model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'],loc = 'upper left')
plt.show()
# predictingimage = "D:/compCarsThesisData/data/image/78/3/2010/0ba8d018cdc994.jpg" #67/1698/2010/6805eb92ac6c70.jpg"
predictImageRead = X_train
# resizingImage = cv2.cv2.resize(predictImageRead,(224,224))
# reshapedFinalImage = np.expand_dims(predictImageRead, axis=0)
# print(reshapedFinalImage.shape)
# npimage = np.array(reshapedFinalImage)
m = model.predict(predictImageRead)
print(m)
print(predictImageRead.shape)
print(train_labels_hotEncode)
# print(m.shape)
plt.imshow(predictImageRead[1])
plt.show()
# n = np.argmax(m,axis=-1)
# n = np.array(m)
print(confusion_matrix(X_test,m[0]))
cm = confusion_matrix(X_test,m[0])
plt.imshow(cm)
plt.show()
Please guide me through this.
Thanks!

Answer 1

If you want to compute a confusion matrix of your training data you have to make your moddel predict all your training examples, roughly like this:
m = model.predict(train_data) # train_data should have the shape (4162, 224, 224, 3)
m should then have a length of 4162 and you can plot the confusion matrix like this:
cm = confusion_matrix(train_labels_Encode, m)
plt.imshow(cm)
plt.show()