验证准确性并没有变得更好

Question

我正在做一个关于神经网络的项目，并正在尝试使用 keras 和 tensorflow 包的 python 代码。目前，我遇到了一个问题，即根本没有使验证准确度上升。我有一个包含 9815 个图像和 200 个测试集图像的训练集。我真的被困在这里请帮忙。

目前，几乎所有 100 个 epoch 的验证结果都恰好是 0.5000，而且根本没有上升。

#Image Processing Stage
train_data = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)

test_data = ImageDataGenerator(rescale = 1./255)

training_set = train_data.flow_from_directory('dataset/train_data', target_size = (128, 128),  batch_size = 42,  class_mode = 'binary')

test_set = test_data.flow_from_directory('dataset/test_data', target_size = (128, 128), batch_size = 42, class_mode = 'binary')




# Starting Convolutional Neural Network
start_cnn = load_model('CNN.h5')
start_cnn.get_weights()
start_cnn = Sequential()


start_cnn.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu', padding='same'))                 #3*3*3*32+32
start_cnn.add(Conv2D(32, (3, 3), activation = 'relu'))
start_cnn.add(MaxPooling2D(pool_size = (2, 2)))

for i in range(0,2):
    start_cnn.add(Conv2D(128, (3, 3), activation = 'relu', padding='same'))

start_cnn.add(MaxPooling2D(pool_size = (2, 2)))

for i in range(0,2):
    start_cnn.add(Conv2D(128, (3, 3), activation = 'relu', padding='same'))

start_cnn.add(MaxPooling2D(pool_size = (2, 2)))

# Flattening
start_cnn.add(Flatten())

# Step 4 - Full connection
start_cnn.add(Dense(activation="relu", units=128))
start_cnn.add(Dense(activation="relu", units=64))
start_cnn.add(Dense(activation="relu", units=32))
start_cnn.add(Dense(activation="softmax", units=1))

start_cnn.summary()


# Compiling the CNN

start_cnn.compile(Adam(learning_rate=0.001), loss = 'binary_crossentropy', metrics = ['accuracy'])

start_cnn.fit(training_set, steps_per_epoch=234, epochs = 100, validation_data = test_set)  


start_cnn.save('CNN.h5')

Answer 1

你不能对一个神经元使用softmax 激活，就像你在这里做的那样：

start_cnn.add(Dense(activation="softmax", units=1))

要对一个神经元进行二元分类，您必须使用sigmoid 激活：

start_cnn.add(Dense(activation="sigmoid", units=1))