神经网络：如果我的batch_size影响准确性，这意味着什么？

Question

我一直听说batch_size不会影响学习的准确性或质量。

但在我的简单模型中，立即使用20,000的batch_size（总数据集250万）会导致NaN损失和0.004精度。 将bath_size减少到2,000将使我失去4.10和0.07的准确度，并且准确性在每个时期得到预期的适当增加。

那么：我的batch_size影响准确性的事实是否意味着我的模型设置错误了？

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(26, activation='relu', input_dim=X.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(Y.shape[1], activation='softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy',])

model.fit(X, Y,epochs=15,batch_size=2000)
score = model.evaluate(X, Y, batch_size=2000)
score

Epoch 1/15
44s - loss: 4.7164 - acc: 0.0781
Epoch 2/15
45s - loss: 4.3305 - acc: 0.0977
Epoch 3/15
45s - loss: 4.1886 - acc: 0.1065
Epoch 4/15
45s - loss: 4.1235 - acc: 0.1104
Epoch 5/15
45s - loss: 4.0881 - acc: 0.1122
Epoch 6/15
45s - loss: 4.0657 - acc: 0.1136
Epoch 7/15
45s - loss: 4.0506 - acc: 0.1148
Epoch 8/15
45s - loss: 4.0393 - acc: 0.1154
Epoch 9/15
47s - loss: 4.0305 - acc: 0.1159
Epoch 10/15


model.fit(X, Y,epochs=15,batch_size=200000)
score = model.evaluate(X, Y, batch_size=200000)
score

Epoch 1/15
42s - loss: NaN - acc: 0.0004