CIFAR-10 在 Keras 上使用 MobileNet/VGG16 的准确度不能超过 10%
[英]CIFAR-10 can't get above 10% Accuracy with MobileNet/VGG16 on Keras
问题描述
I'm trying to train the mobileNet and VGG16 models with the CIFAR10-dataset but the accuracy can't get above 9,9%.
我正在尝试使用 CIFAR10 数据集训练 mobileNet 和 VGG16 模型，但准确率不能超过 9.9%。 I need it with the completly model (include_top=True) and without the wights from imagenet.我需要它与完全 model (include_top=True) 和没有来自 imagenet 的 wights。
PS: I have tried increasing/decreasing dropout and learning rate and I changed the optimizers but I become always the same accuracy. PS：我尝试增加/减少 dropout 和学习率，并更改了优化器，但我始终保持相同的准确性。
train_generator = ImageDataGenerator(
rotation_range=2,
horizontal_flip=True,
zoom_range=.1 )
val_generator = ImageDataGenerator(
rotation_range=2,
horizontal_flip=True,
zoom_range=.1)
train_generator.fit(x_train)
val_generator.fit(x_val)
batch_size= 100
epochs=50
learn_rate=.001
sgd=SGD(lr=learn_rate,momentum=.9,nesterov=False)
adam=Adam(lr=learn_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model_1.compile(optimizer=adam,loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model_1.fit_generator(train_generator.flow(x_train,y_train,batch_size=batch_size),
epochs=epochs,
steps_per_epoch=x_train.shape[0]//batch_size,
validation_data=val_generator.flow(x_val,y_val,batch_size=batch_size),validation_steps=250,
verbose=1)
Results of MobileNet: MobileNet 的结果：
Epoch 1/50
350/350 [==============================] - 17s 50ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1021
Epoch 2/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1030
Epoch 3/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1016
Epoch 4/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1014
Epoch 5/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1040
Epoch 6/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1009
Epoch 7/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1035
Epoch 8/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1013
Epoch 9/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1029
Epoch 10/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1023
Epoch 11/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1017
Epoch 12/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1020
Epoch 13/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1020
Epoch 14/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1033
Epoch 15/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1011
Epoch 16/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1016
Epoch 17/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1024
Epoch 18/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1024
Epoch 19/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1041
Epoch 20/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1010
Epoch 21/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1022
Epoch 22/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1014
Epoch 23/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1035
Epoch 24/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1032
Epoch 25/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1012
Epoch 26/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1018
Epoch 27/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1022
Epoch 28/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1031
Epoch 29/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1022
Epoch 30/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1015
Epoch 31/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1028
Epoch 32/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1015
Epoch 33/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1030
Epoch 34/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1003
Epoch 35/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1044
Epoch 36/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1012
Epoch 37/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1022
Epoch 38/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1021
Epoch 39/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1028
Epoch 40/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1012
Epoch 41/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1035
Epoch 42/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1009
Epoch 43/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1034
Epoch 44/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1024
Epoch 45/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1016
Epoch 46/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1028
Epoch 47/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1016
Epoch 48/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1033
Epoch 49/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1018
Epoch 50/50
350/350 [==============================] - 17s 49ms/step - loss: nan - accuracy: 0.0990 - val_loss: nan - val_accuracy: 0.1023
<tensorflow.python.keras.callbacks.History at 0x7fa30b188e48>
Is there any solution to solve this?有没有办法解决这个问题？
1 个解决方案
解决方案1
0 2020-07-17 00:45:42
I use the MobileNet model often and it works well.我经常使用 MobileNet model，它运行良好。 To use it see the code below.要使用它，请参阅下面的代码。
mobile = tf.keras.applications.mobilenet.MobileNet( include_top=False,
input_shape=(image_size,image_size,3),
pooling='max', weights='imagenet',
alpha=1, depth_multiplier=1,dropout=.4)
x=mobile.layers[-1].output
x=keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)
predictions=Dense (len(classes), activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=predictions)
for layer in model.layers:
layer.trainable=True
model.compile(Adamax(lr=lr_rate), loss='categorical_crossentropy', metrics=['accuracy'])
Classes is the number of classes in the dataset.类是数据集中的类数。 Image size is the size of the image in pixels.s图像大小是图像的大小，以像素.s 为单位
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