Я могу воссоздать вашу ошибку, используя набор данных Dogs_Vs_Cats. Если у меня 2000 образцов в каталоге поездов и 400 образцов в каталоге проверки.
Пожалуйста, измените model.predict_generator с
Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size+1)
на
Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size)
решит это ValueError: Found input variables with inconsistent numbers of samples: [400, 440]
Полный код см. Ниже
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import numpy as np
from keras.preprocessing import image
from sklearn.metrics import classification_report, confusion_matrix
from google.colab import drive
drive.mount('/content/drive')
train_data_dir = '/content/drive/My Drive/Dogs_Vs_Cats/train'
validation_data_dir = '/content/drive/My Drive/Dogs_Vs_Cats/validation'
img_width, img_height = 150, 150
nb_train_samples = 2000
nb_validation_samples = 400
epochs = 10
batch_size = 40 #16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
train_datagen = ImageDataGenerator(
rescale = 1. / 255,
shear_range = 0.2,
zoom_range= 0.2,
horizontal_flip= True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = test_datagen.flow_from_directory(
train_data_dir,
target_size= (img_width, img_height),
batch_size= batch_size,
class_mode= 'binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size= (img_width, img_height),
batch_size= batch_size,
class_mode= 'binary')
model = Sequential()
model.add(Conv2D(32, (3, 3), strides = (1, 1), input_shape = input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), strides = (1, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.summary()
model.compile(loss = 'binary_crossentropy',
optimizer = 'rmsprop',
metrics = ['accuracy'])
model.fit_generator(
train_generator,
steps_per_epoch= nb_train_samples // batch_size,
epochs= epochs,
validation_data= validation_generator,
validation_steps= nb_validation_samples // batch_size)
Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
Вывод:
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Found 2000 images belonging to 2 classes.
Found 400 images belonging to 2 classes.
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_9 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
activation_9 (Activation) (None, 148, 148, 32) 0
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
activation_10 (Activation) (None, 72, 72, 64) 0
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 36, 36, 64) 0
_________________________________________________________________
flatten_5 (Flatten) (None, 82944) 0
_________________________________________________________________
dense_9 (Dense) (None, 64) 5308480
_________________________________________________________________
dropout_5 (Dropout) (None, 64) 0
_________________________________________________________________
dense_10 (Dense) (None, 1) 65
=================================================================
Total params: 5,327,937
Trainable params: 5,327,937
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
50/50 [==============================] - 12s 233ms/step - loss: 0.9345 - accuracy: 0.5375 - val_loss: 0.6303 - val_accuracy: 0.5225
Epoch 2/10
50/50 [==============================] - 11s 226ms/step - loss: 0.6745 - accuracy: 0.5965 - val_loss: 0.6094 - val_accuracy: 0.6725
Epoch 3/10
50/50 [==============================] - 11s 223ms/step - loss: 0.6196 - accuracy: 0.6605 - val_loss: 0.5694 - val_accuracy: 0.7150
Epoch 4/10
50/50 [==============================] - 11s 223ms/step - loss: 0.5501 - accuracy: 0.7285 - val_loss: 0.6216 - val_accuracy: 0.7225
Epoch 5/10
50/50 [==============================] - 11s 221ms/step - loss: 0.4794 - accuracy: 0.7790 - val_loss: 0.6268 - val_accuracy: 0.6025
Epoch 6/10
50/50 [==============================] - 11s 226ms/step - loss: 0.4038 - accuracy: 0.8195 - val_loss: 0.4842 - val_accuracy: 0.6975
Epoch 7/10
50/50 [==============================] - 11s 222ms/step - loss: 0.3207 - accuracy: 0.8595 - val_loss: 0.5600 - val_accuracy: 0.7325
Epoch 8/10
50/50 [==============================] - 13s 257ms/step - loss: 0.2574 - accuracy: 0.8920 - val_loss: 0.9705 - val_accuracy: 0.7525
Epoch 9/10
50/50 [==============================] - 13s 252ms/step - loss: 0.2049 - accuracy: 0.9235 - val_loss: 0.7311 - val_accuracy: 0.7475
Epoch 10/10
50/50 [==============================] - 13s 251ms/step - loss: 0.1448 - accuracy: 0.9515 - val_loss: 1.0541 - val_accuracy: 0.7150
Confusion Matrix
[[200 0]
[200 0]]
Надеюсь, это ответит на ваш вопрос. Если нет, пожалуйста, поделитесь полной трассировкой и кодом для отладки, я рад вам помочь.