Keras CNN模型总是返回[0.0.5 0.5]

Question

有人能帮我解决这个问题吗？我的模型总是返回1个类。源代码如下：我想对图像进行分类(二进制(。该模型产生了良好的准确性。现在，我需要测试模型中的哪些新图像，我加载了模型并尝试预测类，但它总是返回0。

batch_size = 30
epochs = 50
IMG_HEIGHT = 224
IMG_WIDTH = 224
image_gen_train = ImageDataGenerator(
rotation_range=15,
width_shift_range=0.01,
height_shift_range=0.01,
rescale=1./255,
shear_range=0.1,
fill_mode='nearest',
validation_split=0.2)

train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
directory=dataset_dir,
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
subset='training',
class_mode='binary') # set as training data

val_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
directory=dataset_dir,
shuffle=False,
target_size=(IMG_HEIGHT, IMG_WIDTH),
subset='validation',
class_mode='binary') # set as validation data

sample_training_images, _ = next(train_data_gen)
# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
fig, axes = plt.subplots(1, 4, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
ax.axis('off')
plt.tight_layout()
plt.savefig('xray_new.png')
plt.clf()
plotImages(sample_training_images[:4])

#the model
model = Sequential()
model.add(Conv2D(64, kernel_size= (3,3), input_shape=(IMG_HEIGHT, IMG_WIDTH, 3),padding='same'))

model.add(BatchNormalization(momentum=0.5, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(64, kernel_size=(3,3), padding='same'))
model.add(BatchNormalization(momentum=0.1, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))

model.add(Conv2D(128, kernel_size =(3,3),padding='same'))
model.add(BatchNormalization(momentum=0.2, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))
model.add(BatchNormalization(momentum=0.1, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(128,(3,3), padding='same' ))
model.add(BatchNormalization(momentum=0.1, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))

model.add(Conv2D(256, kernel_size = (3,3), padding='same'))
model.add(BatchNormalization(momentum=0.2, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))
model.add(Conv2D(256, kernel_size= (3,3) ,padding='same'))
model.add(BatchNormalization(momentum=0.1, epsilon=1e-5, gamma_initializer="uniform"))
model.add(LeakyReLU(alpha=0.1))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))

model.add(Flatten())
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.1))
model.add(BatchNormalization())
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
#model.summary()
model.save("model.h5")
history = model.fit_generator(
train_data_gen,
steps_per_epoch= train_data_gen.samples // batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps= val_data_gen.samples // batch_size,verbose=1)

但当我测试模型时，它总是输出1个类：

filepath = 'model.h5'
model = load_model(filepath,compile=True)
def test(model,image_path):

test_image = image.load_img(image_path, target_size=(IMG_HEIGHT, IMG_WIDTH))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)

# predict the result
prediction = model.predict(test_image)

print(prediction)
if prediction[0][0] == 1:
my = 'Normal'
else:
my = 'Asthma'
print(my)
prediction = np.argmax(prediction)
labels = (train_data_gen.class_indices)
labels = dict((v,k) for k,v in labels.items())
return labels[prediction]

我真的很感谢你的帮助！

Answer 1

我想您在测试部分忘记了将输入图像除以255.。

Answer 2

找到预测值后，不要将其与1进行核对。CCD_ 2总是返回一个介于0和1之间的值。

因此，您可以将if条件更改为以下条件：

if prediction[0][0] > 0.5: my = 'Normal'
else: my = 'asthama'
我认为这应该能解决你的问题。