tensorflow 准确率，val_accuracy 在训练时保持不变答案

【问题标题】：tensorflow accuracy, val_accuracy remains the same while trainingtensorflow 准确率，val_accuracy 在训练时保持不变
【发布时间】：2020-10-25 00:41:15
【问题描述】：

我基于Chest X-Ray Images (Pneumonia) dataset 构建了一个 CNN，由于某种原因，当我训练模型时，我在各个时期都获得了相同的准确度和 val_accuracy。

train_ds = ImageDataGenerator()
traindata = train_ds.flow_from_directory(directory="../input/chest-xray-pneumonia/chest_xray/train",target_size=(IMG_HEIGHT,IMG_WIDTH),shuffle=True)
// Found 5216 images belonging to 2 classes.

test_ds = ImageDataGenerator()
testdata = test_ds.flow_from_directory(directory="../input/chest-xray-pneumonia/chest_xray/test",target_size=(IMG_HEIGHT,IMG_WIDTH),shuffle=True)
//Found 624 images belonging to 2 classes.

model = keras.Sequential([
    keras.layers.Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding="same", activation="relu"),
    keras.layers.Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)),
    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)),
    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"),
    keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)),
    keras.layers.Flatten(),
    keras.layers.Dense(units=4096,activation="relu"),
#     keras.layers.Dropout(.5),
    keras.layers.Dense(units=4096,activation="relu"),
#     keras.layers.Dropout(.5),
    keras.layers.Dense(units=2, activation="softmax"),
])

opt = keras.optimizers.Adam(lr=0.001)

model.compile(optimizer=opt,
            loss="categorical_crossentropy",
            metrics=['accuracy'])


logdir = "logs\\training\\" + datetime.now().strftime("%Y%m%d-%H%M%S")

checkpoint = keras.callbacks.ModelCheckpoint("vgg16_1.h5", verbose=1, monitor='val_accuracy', save_best_only=True, mode='auto')
early = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)


hist = model.fit(traindata, 
      steps_per_epoch=STEPS_PER_EPOCH,
      epochs=100,
      validation_data=testdata,
      validation_steps=VALIDATION_STEPS,
      callbacks=[early, tensorboard_callback])

Epoch 1/100
163/163 [==============================] - 172s 1s/step - loss: 62.6885 - accuracy: 0.7375 - val_loss: 0.6827 - val_accuracy: 0.6250
Epoch 2/100
163/163 [==============================] - 157s 961ms/step - loss: 0.5720 - accuracy: 0.7429 - val_loss: 0.7133 - val_accuracy: 0.6250
Epoch 3/100
163/163 [==============================] - 159s 975ms/step - loss: 0.5725 - accuracy: 0.7429 - val_loss: 0.6691 - val_accuracy: 0.6250
Epoch 4/100
163/163 [==============================] - 159s 973ms/step - loss: 0.5721 - accuracy: 0.7429 - val_loss: 0.7036 - val_accuracy: 0.6250
Epoch 5/100
163/163 [==============================] - 158s 971ms/step - loss: 0.5715 - accuracy: 0.7429 - val_loss: 0.7169 - val_accuracy: 0.6250
Epoch 6/100
163/163 [==============================] - 160s 983ms/step - loss: 0.5718 - accuracy: 0.7429 - val_loss: 0.6982 - val_accuracy: 0.6250

我尝试更改最后一层的激活函数，添加 dropout 层并调整神经元的数量，但似乎没有任何效果。有没有人知道是什么导致了这种奇怪的行为？

【问题讨论】：

标签： tensorflow keras conv-neural-network training-data

【解决方案1】：

您只有一个小数据集。从您的训练损失的外观来看，我认为您的网络实际上已经在 1 个 epoch 之后收敛。（有大量的过拟合）

对于您拥有的数据量，我建议尝试使用更小的网络，或使用数据增强技术来规范您的模型。

【讨论】：