问题描述
我正在尝试训练数据集，并在训练完数据集后输出混淆矩阵。
这是代码
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Conv2D,Flatten,Dropout,MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import Input
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import softmax
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Convolution2D
import os
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy import signal
from scipy.signal import chirp
import numpy.fft
from numpy.fft import fft as rf
import random
import pandas as pd
import sklearn.model_selection as model_selection
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import Input
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import softmax
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Convolution2D
import os
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy import signal
from scipy.signal import chirp
import numpy.fft
from numpy.fft import fft as rf
import random
import pandas as pd
import sklearn.model_selection as model_selection
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split
from PIL import Image
import imageio as io
import glob
from matplotlib import image
import h5py
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Activation,concatenate
from tensorflow.keras.layers import Flatten,Dropout
from tensorflow.keras.layers import Convolution2D,MaxPooling2D
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Input,Concatenate,\
MaxPool2D,GlobalAvgPool2D,Activation
def squeezenet(input_shape,n_classes):
def fire(x,fs,fe):
s = Conv2D(fs,1,activation='relu')(x)
e1 = Conv2D(fe,activation='relu')(s)
e3 = Conv2D(fe,3,padding='same',activation='relu')(s)
output = Concatenate()([e1,e3])
return output
input = Input(input_shape)
x = Conv2D(96,7,strides=2,activation='relu')(input)
x = MaxPool2D(3,padding='same')(x)
x = fire(x,16,64)
x = fire(x,32,128)
x = MaxPool2D(3,128)
x = fire(x,48,192)
x = fire(x,64,256)
x = fire(x,256)
x = MaxPool2D(3,padding='same')(x)
x = Dropout(0.6)(x)
x = Conv2D(n_classes,1)(x)
x = GlobalAvgPool2D()(x)
x = Flatten()(x)
output = Activation('softmax')(x)
model = Model(input,output)
return model
import pathlib
import PIL
test_datagen = ImageDataGenerator(rescale=1./255)
data_dir = os.path.join(r"location/directory of the file","file")
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
print(image_count)
rect = list(data_dir.glob('Rect/*'))
PIL.Image.open(str(rect[1]))
batch_size = 32
img_height = 227
img_width = 227
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,validation_split=0.1,subset="training",seed=123,image_size=(img_height,img_width),batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,subset="validation",batch_size=batch_size)
class_names = train_ds.class_names
print(range(len(class_names)))
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
normalized_ds = train_ds.map(lambda x,y: (normalization_layer(x),y))
image_batch,labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are Now in `[0,1]`.
print(np.min(first_image),np.max(first_image))
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping,ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
model = squeezenet((227,227,3),2)
sgd = SGD(lr=0.001,decay=0.0002,momentum=0.9,nesterov=True)
model.compile(
optimizer=sgd,loss='binary_crossentropy',metrics=['accuracy'])
print(model.summary())
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs_range = epoch #range(epochs)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.plot(epochs_range,acc,label='Training Accuracy')
plt.plot(epochs_range,val_acc,label='Validation Accuracy')
plt.legend(loc='lower right')
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs_range = epoch #range(epochs)
plt.figure(figsize=(8,label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1,2)
plt.plot(epochs_range,loss,label='Training Loss')
plt.plot(epochs_range,val_loss,label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
plt.title('Training and Validation Accuracy')
plt.subplot(1,label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
#supply a imafe to classifer to get an image out
#calculate the confusion matrix manualy
from sklearn.metrics import classification_report,confusion_matrix
Y_pred = model.predict_generator(val_ds,720 // 32+1)
y_pred = np.argmax(Y_pred,axis=1)
print(y_pred.shape)
print('Confusion Matrix')
print(confusion_matrix(class_names,y_pred))
print('Classification Report')
target_names = ['Cats','Dogs','Horse']
print(classification_report(class_names,y_pred,target_names=target_names))
这是我遇到的错误。
ValueError Traceback (most recent call last)
<ipython-input-9-5188ce05905a> in <module>
9 print(y_pred.shape)
10 print('Confusion Matrix')
---> 11 print(confusion_matrix(class_names,y_pred))
12 print('Classification Report')
13 target_names = ['Cats','Horse']
in confusion_matrix(y_true,labels,sample_weight)
251
252 """
--> 253 y_type,y_true,y_pred = _check_targets(y_true,y_pred)
254 if y_type not in ("binary","multiclass"):
255 raise ValueError("%s is not supported" % y_type)
in _check_targets(y_true,y_pred)
69 y_pred : array or indicator matrix
70 """
---> 71 check_consistent_length(y_true,y_pred)
72 type_true = type_of_target(y_true)
73 type_pred = type_of_target(y_pred)
in check_consistent_length(*arrays)
203 if len(uniques) > 1:
204 raise ValueError("Found input variables with inconsistent numbers of"
--> 205 " samples: %r" % [int(l) for l in lengths])
206
207
ValueError: Found input variables with inconsistent numbers of samples: [3,360]
解决方法
在confusion_matrix方法中，您的参数应该为y_true和y_pred，就像您可以找到的in the doc一样。
似乎您的第一个参数y_true（= class_names）的大小为3，第二个参数y_pred的大小为360。但是，自{{1} }是您分类的估计结果，而y_pred是分类的基本事实。
以下是与sklearn相关的示例，使用了3个类：y_true
[0,1,2]编辑
from sklearn.metrics import confusion_matrix
y_true = [2,2,1]
y_pred = [0,2]
confusion_matrix(y_true,y_pred)
的结构如何？
通常y_true包含与每个输入对应的每个标签。您似乎给分类器提供了360个输入，因此对于每个输入，您都应该有一个关联的标签，即y_true之一。包含输入的真实标签的完整向量为class_names。