问题描述
我正在尝试使用tensorflow 2.3设置一个混淆矩阵这是我得到的错误
ValueError Traceback (most recent call last)
<ipython-input-11-29e008512d27> in <module>
8 y_pred = np.argmax(Y_pred,axis=1)
9 print('Confusion Matrix')
---> 10 print(confusion_matrix(class_names,y_pred))
11 print('Classification Report')
12 target_names = ['Cats','Dogs','Horse']
in confusion_matrix(y_true,y_pred,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]
这里是代码要看混淆矩阵代码，请先结束，但除此之外，这就是代码中的全部内容。我不知道如何解决此错误，所以任何事情都可以帮忙
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("The directory before the join","the oin directory")
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(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())
history = model.fit(
train_ds,validation_data=val_ds,epochs=100)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs_range = range(100) #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 = range(100) #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('Confusion Matrix')
print(confusion_matrix(class_names,y_pred))
print('Classification Report')
target_names = ['Cats','Horse']
print(classification_report(class_names,target_names=target_names))
有问题的代码旨在成为python中的squeezenet实现，而我想做的就是看看我在matlab中实现的精度是否可以在python中实现。
解决方法
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