我正在训练一个简单的MLP来使用Keras对MNIST数字进行分类.我遇到了一个问题,无论我使用什么优化器和学习率,模型都不会学习/下降,我的准确性几乎和随机猜测一样好.
这是代码:
model2=Sequential()
model2.add(Dense(output_dim=512, input_dim=784, activation='relu', name='dense1', kernel_initializer='random_uniform'))
model2.add(Dropout(0.2, name='dropout1'))
model2.add(Dense(output_dim=512, input_dim=512, activation='relu', name='dense2', kernel_initializer='random_uniform'))
model2.add(Dropout(0.2, name='dropout2'))
model2.add(Dense(output_dim=10, input_dim=512, activation='softmax', name='dense3', kernel_initializer='random_uniform'))
model2.compile(optimizer=Adagrad(), loss='categorical_crossentropy', metrics=['accuracy'])
model2.summary()
model2.fit(image_train.as_matrix(),img_keras_lb,batch_size=128,epochs = 100)
和输出:
Epoch 1/100
33600/33600 [==============================] - 5s - loss: 14.6704 - acc: 0.0894
Epoch 2/100
33600/33600 [==============================] - 4s - loss: 14.6809 - acc: 0.0892
Epoch 3/100
33600/33600 [==============================] - 4s - loss: 14.6809 - acc: 0.0892
Epoch 4/100
33600/33600 [==============================] - 4s - loss: 14.6809 - acc: 0.0892
Epoch 5/100 …