第二個epoch的初始損失與第一個epoch的最終損失不一致。每個時期的損失和準確性保持不變-有解無憂

第二個損失與第一個時期不一致。之后，每個初始損失在每個時期始終保持不變。所有這些引數都保持不變。我有一些深度學習的背景，但這是我第一次實作我自己的模型，所以我想直觀地知道我的模型出了什么問題。資料集是裁剪后的人臉，有兩個分類，每個分類有 300 張圖片。我非常感謝您的幫助。

import tensorflow as tf
from tensorflow import keras
from IPython.display import Image
import matplotlib.pyplot as plt
from keras.layers import ActivityRegularization
from keras.layers import Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator

image_generator = ImageDataGenerator(
    featurewise_center=False, samplewise_center=False,
    featurewise_std_normalization=False, samplewise_std_normalization=False,
    rotation_range=0, width_shift_range=0.0, height_shift_range=0.0,
    brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0,
    horizontal_flip=False, vertical_flip=False, rescale=1./255
)

image = image_generator.flow_from_directory('./util/untitled folder',batch_size=938)

x, y = image.next()
x_train = x[:500]
y_train = y[:500]
x_test = x[500:600]
y_test = y[500:600]

train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(4)
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(4)

plt.imshow(x_train[0])

def convolutional_model(input_shape):

    input_img = tf.keras.Input(shape=input_shape)
    x = tf.keras.layers.Conv2D(64, (7,7), padding='same')(input_img)
    x = tf.keras.layers.BatchNormalization(axis=3)(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=1, padding='same')(x)
    x = Dropout(0.5)(x)
    x = tf.keras.layers.Conv2D(128, (3, 3), padding='same', strides=1)(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', strides=4)(x)
    x = tf.keras.layers.Flatten()(x)
    x = ActivityRegularization(0.1,0.2)(x)
    outputs = tf.keras.layers.Dense(2, activation='softmax')(x)


    model = tf.keras.Model(inputs=input_img, outputs=outputs)
    return model


conv_model = convolutional_model((256, 256, 3))
conv_model.compile(loss=keras.losses.categorical_crossentropy,
                   optimizer=keras.optimizers.SGD(lr=1),
                   metrics=['accuracy'])
conv_model.summary()

conv_model.fit(train_dataset,epochs=100, validation_data=test_dataset)


Epoch 1/100
    2021-12-23 15:06:22.165763: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
    2021-12-23 15:06:22.172255: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
    125/125 [==============================] - ETA: 0s - loss: 804.6805 - accuracy: 0.48602021-12-23 15:06:50.936870: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
    125/125 [==============================] - 35s 275ms/step - loss: 804.6805 - accuracy: 0.4860 - val_loss: 0.7197 - val_accuracy: 0.4980
    Epoch 2/100
    125/125 [==============================] - 34s 270ms/step - loss: 0.7360 - accuracy: 0.4820 - val_loss: 0.7197 - val_accuracy: 0.4980
    Epoch 3/100
    125/125 [==============================] - 34s 276ms/step - loss: 0.7360 - accuracy: 0.4820 - val_loss: 0.7197 - val_accuracy: 0.4980

uj5u.com熱心網友回復：

由于您具有恒定的損失準確率，因此您的網路很可能沒有學到任何東西（因為您有兩個類，它總是預測其中一個）。

最后一層的激活函式、損失函式和神經元數量是正確的。

問題與您加載影像的方式無關，而與學習率 1 相關。在如此高的學習率下，網路不可能學習任何東西。

您應該從更小的學習率開始，例如0.0001或0.00001，然后如果性能仍然很差，則嘗試除錯資料加載程序。

uj5u.com熱心網友回復：

我很確定它與您加載資料的方式有關，更具體地說是與x, y = image.next()零件有關。如果您能夠將資料拆分./util/untitled folder為分別具有訓練和驗證資料的單獨檔案夾，您可以在管道上使用與Tensorflow 頁面上的示例部分相同的型別：

train_datagen = ImageDataGenerator(
    featurewise_center=False, 
    samplewise_center=False,
    featurewise_std_normalization=False, 
    samplewise_std_normalization=False,
    rotation_range=0, 
    width_shift_range=0.0, 
    height_shift_range=0.0,
    brightness_range=None, 
    shear_range=0.0, 
    zoom_range=0.0, 
    channel_shift_range=0.0,
    horizontal_flip=False, 
    vertical_flip=False, 
    rescale=1./255)
test_datagen = ImageDataGenerator(featurewise_center=False, 
    samplewise_center=False,
    featurewise_std_normalization=False, 
    samplewise_std_normalization=False,
    rotation_range=0, 
    width_shift_range=0.0, 
    height_shift_range=0.0,
    brightness_range=None, 
    shear_range=0.0, 
    zoom_range=0.0, 
    channel_shift_range=0.0,
    horizontal_flip=False, 
    vertical_flip=False, 
    rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(256, 256),
    batch_size=4)
validation_generator = test_datagen.flow_from_directory(
    'data/validation',
    target_size=(256, 256),
    batch_size=4)
model.fit(
    train_generator,
    epochs=100,
    validation_data=validation_generator)

轉載請註明出處，本文鏈接：https://www.uj5u.com/yidong/391925.html

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