keras不考慮batch_input參數

我正在訓練一個帶有keras的神經網絡，它似乎沒有正確解釋batch_size參數。keras不考慮batch_input參數

請參閱下面的代碼（應用程序是愚蠢的，我關心的是輸出）。

import numpy as np 
from keras.models import Sequential 
from keras.layers import Activation, Dense, Reshape 
import keras 

class LossHistory(keras.callbacks.Callback): 
    def on_train_begin(self, logs={}): 
     self.losses = [] 

    def on_batch_end(self, batch, logs={}): 
     self.losses.append(logs.get('loss')) 

history = LossHistory() 


X = np.random.normal(0, 1, (1000, 2)) 
Y = np.random.normal(0, 1, (1000, 3)) 

model = Sequential() 
model.add(Dense(20, input_shape = (2,), name='input layer dude')) 
model.add(Activation('relu')) 
model.add(Dense(12)) 
model.add(Activation('relu')) 
model.add(Dense(8)) 
model.add(Activation('linear')) 
model.add(Dense(3)) 
model.add(Activation('linear')) 
model.add(Reshape(target_shape=(3,), name='output layer dude')) 
model.compile(optimizer='adam', loss='mse',)

當我通過調用這個模式：

model.fit(X, Y, batch_size=10, nb_epoch=10, callbacks=[history])

輸出似乎表明，它是不是做每批次10個項目，而1000（這是總樣本數）。

Epoch 1/10 
1000/1000 [==============================] - 0s - loss: 898.6197  
Epoch 2/10 
1000/1000 [==============================] - 0s - loss: 31.5123  
Epoch 3/10 
1000/1000 [==============================] - 0s - loss: 16.7140  
Epoch 4/10 
1000/1000 [==============================] - 0s - loss: 11.4034  
Epoch 5/10 
1000/1000 [==============================] - 0s - loss: 8.9275  
Epoch 6/10 
1000/1000 [==============================] - 0s - loss: 7.4699  
Epoch 7/10 
1000/1000 [==============================] - 0s - loss: 6.5648  
Epoch 8/10 
1000/1000 [==============================] - 0s - loss: 5.9576  
Epoch 9/10 
1000/1000 [==============================] - 0s - loss: 5.5064  
Epoch 10/10 
1000/1000 [==============================] - 0s - loss: 5.1514

任何線索發生了什麼問題？

來源

2016-09-14 cantdutchthis

他實際上正在考慮它。一個時代是對整個數據集的迭代，因此是1000/1000。

我改變了批量大小爲128多一點可讀性，增加了一個回調每批次後打印的損失，我所得到的是這樣的（我也增加了數據量以提高可讀性）：

Using Theano backend. 
Using gpu device 1: GeForce GTX 770 (CNMeM is disabled, cuDNN 5105) 
Epoch 1/10 
mbloss 1.00058555603 lr 0.0010000000475 
    128/10000 [..............................] - ETA: 3s - loss: 1.0006 mbloss 1.00051558018 lr 0.0010000000475 
    256/10000 [..............................] - ETA: 4s - loss: 1.0006 mbloss 1.00094401836 lr 0.0010000000475 
    384/10000 [>.............................] - ETA: 4s - loss: 1.0007 mbloss 1.00001847744 lr 0.0010000000475 
    512/10000 [>.............................] - ETA: 3s - loss: 1.0005 mbloss 1.00019526482 lr 0.0010000000475 
    640/10000 [>.............................] - ETA: 3s - loss: 1.0005 mbloss 0.999684214592 lr 0.0010000000475 
    768/10000 [=>............................] - ETA: 3s - loss: 1.0003 mbloss 0.999649345875 lr 0.0010000000475 
    896/10000 [=>............................] - ETA: 3s - loss: 1.0002 mbloss 1.00126934052 lr 0.0010000000475 
1024/10000 [==>...........................] - ETA: 3s - loss: 1.0004 mbloss 1.00039303303 lr 0.0010000000475 
1152/10000 [==>...........................] - ETA: 3s - loss: 1.0004 mbloss 1.00083625317 lr 0.0010000000475 
1280/10000 [==>...........................] - ETA: 3s - loss: 1.0004 mbloss 1.00036990643 lr 0.0010000000475 
1408/10000 [===>..........................] - ETA: 2s - loss: 1.0004 mbloss 0.999625504017 lr 0.0010000000475 
1536/10000 [===>..........................] - ETA: 2s - loss: 1.0003 mbloss 1.0005017519 lr 0.0010000000475 
1664/10000 [===>..........................] - ETA: 2s - loss: 1.0004 mbloss 0.999049901962 lr 0.0010000000475 
1792/10000 [====>.........................] - ETA: 2s - loss: 1.0003 mbloss 0.999758243561 lr 0.0010000000475 
1920/10000 [====>.........................] - ETA: 2s - loss: 1.0002 mbloss 0.99894207716 lr 0.0010000000475 
2048/10000 [=====>........................] - ETA: 2s - loss: 1.0001 mbloss 1.00113630295 lr 0.0010000000475 
2176/10000 [=====>........................] - ETA: 2s - loss: 1.0002 mbloss 0.999107062817 lr 0.0010000000475

如果你需要它，回調到一個批次結束打印的東西：

class MBLossPrint(Callback): 
    def on_batch_end(self, batch, logs={}): 
     print ' mbloss', logs['loss'], 'lr', self.model.optimizer.lr.get_value()

希望這有助於:)

來源

2016-09-15 00:33:21 powder

keras不考慮batch_input參數

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