如何使用tensorflow 2.3建立混淆矩阵？

Question

我试图用tensorflow 2.3建立一个混淆矩阵，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]

下面是代码，看看混淆矩阵代码，直到最后，但这是代码中的全部内容。我不知道如何修复这个错误，所以任何东西都可以帮助我

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, 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

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, Conv2D, 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, 1, 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, padding='same', activation='relu')(input)
  x = MaxPool2D(3, strides=2, padding='same')(x)
  
  x = fire(x, 16, 64)
  x = fire(x, 16, 64)
  x = fire(x, 32, 128)
  x = MaxPool2D(3, strides=2, padding='same')(x)
  
  x = fire(x, 32, 128)
  x = fire(x, 48, 192)
  x = fire(x, 48, 192)
  x = fire(x, 64, 256)
  x = fire(x, 64, 256)
  x = MaxPool2D(3, strides=2, 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,
  validation_split=0.1,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  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, 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')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 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, 2, 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()



#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', 'Dogs', 'Horse']
print(classification_report(class_names, y_pred, target_names=target_names))

所讨论的代码是python中的一个squeezenet实现，我试图做的是看看我在matlab中获得的精度是否可以在python中实现