Using Custom Callback function with Keras

Using Custom Callback function with Keras

2018, Jul 22    

Using Custom Callback function with Keras

You can use history callback function or tensorboard in basic model monitoring. But, if you monitor another model like Recurrent Neural Network, you would not monitor properly because RNN call fit function many times. Let me give you a example.

for epoch in range(1000):
    print("Epochs : " + str(epoch))
    hist = model.fit(x_train, y_train, epochs = 1, batch_size = 1, verbose = 2, shuffle=False)
    model.reset_states()

In above code, You can not observe the trend by every epoch because in every epoch, new history object replaces existing object. Accordingly, You need to define custom callback function in order to maintain the existing learning state. Let me give you a example.

import keras
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np

np.random.seed(3)

Using TensorFlow backend.

Define custom callback function

class CustomHistory(keras.callbacks.Callback):
    def init(self):
        self.train_loss = []
        self.val_loss = []
        self.train_acc = []
        self.val_acc = []
        
    def on_epoch_end(self, batch, logs={}):
        self.train_loss.append(logs.get('loss'))
        self.val_loss.append(logs.get('val_loss'))
        self.train_acc.append(logs.get('acc'))
        self.val_acc.append(logs.get('val_acc'))

Dataset

(x_train, y_train), (x_test, y_test) = mnist.load_data()

print(x_train.shape, y_train.shape)


x_val = x_train[50000:]
y_val = y_train[50000:]
x_train = x_train[:50000]
y_train = y_train[:50000]

preprocessing dataset

x_train = x_train.reshape(50000, 784).astype("float32") / 255.0
x_val = x_val.reshape(10000, 784).astype("float32") / 255.0
x_test = x_test.reshape(10000, 784).astype("float32") / 255.0

Randomize dataset

train_rand_idx = np.arange(0, x_train.shape[0])
val_rand_idx = np.arange(0, x_val.shape[0])
np.random.shuffle(train_rand_idx)
np.random.shuffle(val_rand_idx)

x_train = x_train[train_rand_idx]
y_train = y_train[train_rand_idx]
x_val = x_val[val_rand_idx]
y_val = y_val[val_rand_idx]

One-hot encoding the labels

y_train = np_utils.to_categorical(y_train)
y_val = np_utils.to_categorical(y_val)
y_test = np_utils.to_categorical(y_test)

Modeling

model = Sequential()
model.add(Dense(units = 2, input_dim = 28*28, activation = "relu"))
model.add(Dense(units = 10, activation = "softmax"))

Compile

model.compile(loss = "categorical_crossentropy", optimizer = "sgd", metrics = ["accuracy"])

Learning

Basically, you can set the epoch option in model.fit (ex. model.fit(epochs = 1000)). But in this example, we set the epoch option as 1 and use the for-loop in order to use custom callback function.

custom_hist = CustomHistory()
custom_hist.init()

for epoch in range(100):
    print("Epoch : {}".format(epoch))
    model.fit(x_train, y_train, batch_size=10, epochs=1, validation_data=(x_val, y_val), callbacks=[custom_hist])

output

Epoch : 0
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 18s 356us/step - loss: 1.6571 - acc: 0.3595 - val_loss: 1.4322 - val_acc: 0.4336
Epoch : 1
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.3770 - acc: 0.4512 - val_loss: 1.3054 - val_acc: 0.4753
Epoch : 2
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 341us/step - loss: 1.2902 - acc: 0.4804 - val_loss: 1.2532 - val_acc: 0.4800
Epoch : 3
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 340us/step - loss: 1.2512 - acc: 0.4958 - val_loss: 1.2210 - val_acc: 0.5155
Epoch : 4
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 341us/step - loss: 1.2272 - acc: 0.5108 - val_loss: 1.2011 - val_acc: 0.5208
Epoch : 5
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.2109 - acc: 0.5211 - val_loss: 1.1898 - val_acc: 0.5331
Epoch : 6
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.1984 - acc: 0.5286 - val_loss: 1.1773 - val_acc: 0.5255
Epoch : 7
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.1879 - acc: 0.5352 - val_loss: 1.1646 - val_acc: 0.5427
Epoch : 8
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1785 - acc: 0.5367 - val_loss: 1.1608 - val_acc: 0.5495
Epoch : 9
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 339us/step - loss: 1.1690 - acc: 0.5417 - val_loss: 1.1520 - val_acc: 0.5428
Epoch : 10
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1629 - acc: 0.5435 - val_loss: 1.1432 - val_acc: 0.5444
Epoch : 11
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 321us/step - loss: 1.1579 - acc: 0.5486 - val_loss: 1.1371 - val_acc: 0.5612
Epoch : 12
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1533 - acc: 0.5518 - val_loss: 1.1298 - val_acc: 0.5601
Epoch : 13
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1502 - acc: 0.5558 - val_loss: 1.1327 - val_acc: 0.5557
Epoch : 14
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 321us/step - loss: 1.1464 - acc: 0.5580 - val_loss: 1.1321 - val_acc: 0.5570
Epoch : 15
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 339us/step - loss: 1.1442 - acc: 0.5591 - val_loss: 1.1235 - val_acc: 0.5601
Epoch : 16
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 329us/step - loss: 1.1426 - acc: 0.5599 - val_loss: 1.1267 - val_acc: 0.5700
Epoch : 17
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.1409 - acc: 0.5626 - val_loss: 1.1187 - val_acc: 0.5702
Epoch : 18
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1387 - acc: 0.5636 - val_loss: 1.1202 - val_acc: 0.5729
Epoch : 19
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.1372 - acc: 0.5637 - val_loss: 1.1273 - val_acc: 0.5598
Epoch : 20
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.1356 - acc: 0.5628 - val_loss: 1.1346 - val_acc: 0.5654
Epoch : 21
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1350 - acc: 0.5661 - val_loss: 1.1168 - val_acc: 0.5691
Epoch : 22
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1331 - acc: 0.5660 - val_loss: 1.1166 - val_acc: 0.5670
Epoch : 23
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 328us/step - loss: 1.1309 - acc: 0.5670 - val_loss: 1.1090 - val_acc: 0.5752
Epoch : 24
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 327us/step - loss: 1.1304 - acc: 0.5680 - val_loss: 1.1055 - val_acc: 0.5771
Epoch : 25
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.1293 - acc: 0.5716 - val_loss: 1.1124 - val_acc: 0.5888
Epoch : 26
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.1271 - acc: 0.5732 - val_loss: 1.0965 - val_acc: 0.5913
Epoch : 27
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.1214 - acc: 0.5907 - val_loss: 1.0904 - val_acc: 0.6045
Epoch : 28
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.1118 - acc: 0.6022 - val_loss: 1.0744 - val_acc: 0.6253
Epoch : 29
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 324us/step - loss: 1.1043 - acc: 0.6071 - val_loss: 1.0732 - val_acc: 0.6256
Epoch : 30
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 328us/step - loss: 1.0999 - acc: 0.6114 - val_loss: 1.0706 - val_acc: 0.6260
Epoch : 31
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0965 - acc: 0.6129 - val_loss: 1.0711 - val_acc: 0.6221
Epoch : 32
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 338us/step - loss: 1.0938 - acc: 0.6126 - val_loss: 1.0617 - val_acc: 0.6443
Epoch : 33
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0922 - acc: 0.6152 - val_loss: 1.0614 - val_acc: 0.6338
Epoch : 34
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.0904 - acc: 0.6130 - val_loss: 1.0578 - val_acc: 0.6294
Epoch : 35
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 330us/step - loss: 1.0891 - acc: 0.6160 - val_loss: 1.0613 - val_acc: 0.6228
Epoch : 36
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 327us/step - loss: 1.0878 - acc: 0.6147 - val_loss: 1.0707 - val_acc: 0.6230
Epoch : 37
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 330us/step - loss: 1.0868 - acc: 0.6135 - val_loss: 1.0569 - val_acc: 0.6319
Epoch : 38
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0853 - acc: 0.6147 - val_loss: 1.0479 - val_acc: 0.6326
Epoch : 39
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 338us/step - loss: 1.0839 - acc: 0.6173 - val_loss: 1.0628 - val_acc: 0.6218
Epoch : 40
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 340us/step - loss: 1.0828 - acc: 0.6139 - val_loss: 1.0508 - val_acc: 0.6278
Epoch : 41
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0817 - acc: 0.6161 - val_loss: 1.0510 - val_acc: 0.6324
Epoch : 42
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 328us/step - loss: 1.0810 - acc: 0.6152 - val_loss: 1.0524 - val_acc: 0.6250
Epoch : 43
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0805 - acc: 0.6153 - val_loss: 1.0531 - val_acc: 0.6210
Epoch : 44
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 330us/step - loss: 1.0790 - acc: 0.6187 - val_loss: 1.0538 - val_acc: 0.6205
Epoch : 45
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 328us/step - loss: 1.0786 - acc: 0.6169 - val_loss: 1.0678 - val_acc: 0.6081
Epoch : 46
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.0771 - acc: 0.6166 - val_loss: 1.0447 - val_acc: 0.6251
Epoch : 47
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 338us/step - loss: 1.0768 - acc: 0.6173 - val_loss: 1.0547 - val_acc: 0.6239
Epoch : 48
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 312us/step - loss: 1.0755 - acc: 0.6176 - val_loss: 1.0498 - val_acc: 0.6254
Epoch : 49
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0748 - acc: 0.6154 - val_loss: 1.0620 - val_acc: 0.6143
Epoch : 50
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0746 - acc: 0.6172 - val_loss: 1.0437 - val_acc: 0.6340
Epoch : 51
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0737 - acc: 0.6186 - val_loss: 1.0483 - val_acc: 0.6281
Epoch : 52
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0730 - acc: 0.6184 - val_loss: 1.0449 - val_acc: 0.6273
Epoch : 53
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0730 - acc: 0.6184 - val_loss: 1.0446 - val_acc: 0.6209
Epoch : 54
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 341us/step - loss: 1.0718 - acc: 0.6183 - val_loss: 1.0444 - val_acc: 0.6414
Epoch : 55
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 330us/step - loss: 1.0713 - acc: 0.6194 - val_loss: 1.0584 - val_acc: 0.6139
Epoch : 56
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0710 - acc: 0.6195 - val_loss: 1.0438 - val_acc: 0.6326
Epoch : 57
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.0698 - acc: 0.6204 - val_loss: 1.0426 - val_acc: 0.6230
Epoch : 58
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0696 - acc: 0.6206 - val_loss: 1.0572 - val_acc: 0.6142
Epoch : 59
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0702 - acc: 0.6176 - val_loss: 1.0397 - val_acc: 0.6325
Epoch : 60
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 331us/step - loss: 1.0687 - acc: 0.6198 - val_loss: 1.0413 - val_acc: 0.6375
Epoch : 61
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0690 - acc: 0.6189 - val_loss: 1.0428 - val_acc: 0.6220
Epoch : 62
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0685 - acc: 0.6177 - val_loss: 1.0392 - val_acc: 0.6277
Epoch : 63
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0681 - acc: 0.6181 - val_loss: 1.0340 - val_acc: 0.6382
Epoch : 64
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.0675 - acc: 0.6199 - val_loss: 1.0395 - val_acc: 0.6233
Epoch : 65
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0672 - acc: 0.6208 - val_loss: 1.0368 - val_acc: 0.6169
Epoch : 66
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0672 - acc: 0.6198 - val_loss: 1.0460 - val_acc: 0.6259
Epoch : 67
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 311us/step - loss: 1.0665 - acc: 0.6204 - val_loss: 1.0340 - val_acc: 0.6425
Epoch : 68
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0668 - acc: 0.6199 - val_loss: 1.0432 - val_acc: 0.6250
Epoch : 69
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 327us/step - loss: 1.0666 - acc: 0.6194 - val_loss: 1.0384 - val_acc: 0.6264
Epoch : 70
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.0653 - acc: 0.6183 - val_loss: 1.0482 - val_acc: 0.6227
Epoch : 71
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0648 - acc: 0.6195 - val_loss: 1.0689 - val_acc: 0.6123
Epoch : 72
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 312us/step - loss: 1.0650 - acc: 0.6188 - val_loss: 1.0553 - val_acc: 0.6124
Epoch : 73
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 320us/step - loss: 1.0648 - acc: 0.6194 - val_loss: 1.0650 - val_acc: 0.6157
Epoch : 74
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0642 - acc: 0.6212 - val_loss: 1.0424 - val_acc: 0.6286
Epoch : 75
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0643 - acc: 0.6213 - val_loss: 1.0417 - val_acc: 0.6438
Epoch : 76
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.0640 - acc: 0.6191 - val_loss: 1.0403 - val_acc: 0.6370
Epoch : 77
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0638 - acc: 0.6198 - val_loss: 1.0384 - val_acc: 0.6314
Epoch : 78
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 331us/step - loss: 1.0633 - acc: 0.6216 - val_loss: 1.0417 - val_acc: 0.6293
Epoch : 79
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0637 - acc: 0.6194 - val_loss: 1.0374 - val_acc: 0.6330
Epoch : 80
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 325us/step - loss: 1.0630 - acc: 0.6200 - val_loss: 1.0386 - val_acc: 0.6242
Epoch : 81
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 338us/step - loss: 1.0635 - acc: 0.6208 - val_loss: 1.0404 - val_acc: 0.6279
Epoch : 82
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0624 - acc: 0.6207 - val_loss: 1.0373 - val_acc: 0.6245
Epoch : 83
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0623 - acc: 0.6208 - val_loss: 1.0338 - val_acc: 0.6328
Epoch : 84
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0625 - acc: 0.6222 - val_loss: 1.0397 - val_acc: 0.6194
Epoch : 85
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 325us/step - loss: 1.0625 - acc: 0.6189 - val_loss: 1.0303 - val_acc: 0.6368
Epoch : 86
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 320us/step - loss: 1.0626 - acc: 0.6208 - val_loss: 1.0335 - val_acc: 0.6363
Epoch : 87
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0618 - acc: 0.6199 - val_loss: 1.0343 - val_acc: 0.6401
Epoch : 88
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0610 - acc: 0.6225 - val_loss: 1.0378 - val_acc: 0.6223
Epoch : 89
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0608 - acc: 0.6227 - val_loss: 1.0452 - val_acc: 0.6214
Epoch : 90
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 333us/step - loss: 1.0619 - acc: 0.6203 - val_loss: 1.0379 - val_acc: 0.6225
Epoch : 91
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0608 - acc: 0.6214 - val_loss: 1.0367 - val_acc: 0.6404
Epoch : 92
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 338us/step - loss: 1.0608 - acc: 0.6223 - val_loss: 1.0330 - val_acc: 0.6366
Epoch : 93
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 328us/step - loss: 1.0607 - acc: 0.6229 - val_loss: 1.0444 - val_acc: 0.6356
Epoch : 94
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 336us/step - loss: 1.0605 - acc: 0.6220 - val_loss: 1.0324 - val_acc: 0.6407
Epoch : 95
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 335us/step - loss: 1.0609 - acc: 0.6209 - val_loss: 1.0405 - val_acc: 0.6306
Epoch : 96
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 332us/step - loss: 1.0603 - acc: 0.6216 - val_loss: 1.0351 - val_acc: 0.6411
Epoch : 97
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 16s 325us/step - loss: 1.0602 - acc: 0.6219 - val_loss: 1.0411 - val_acc: 0.6287
Epoch : 98
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 334us/step - loss: 1.0601 - acc: 0.6211 - val_loss: 1.0338 - val_acc: 0.6238
Epoch : 99
Train on 50000 samples, validate on 10000 samples
Epoch 1/1
50000/50000 [==============================] - 17s 337us/step - loss: 1.0597 - acc: 0.6202 - val_loss: 1.0356 - val_acc: 0.6299

Display the results

import matplotlib.pyplot as plt
%matplotlib inline
fig, loss_ax = plt.subplots()

acc_ax = loss_ax.twinx()

loss_ax.plot(custom_hist.train_loss, 'y', label = 'train loss')
loss_ax.plot(custom_hist.val_loss, 'r', label = 'val loss')

acc_ax.plot(custom_hist.train_acc, 'b', label = 'train acc')
acc_ax.plot(custom_hist.val_acc, 'g', label = 'val acc')

loss_ax.set_xlabel('epoch')
loss_ax.set_ylabel('loss')
acc_ax.set_ylabel('accuaracy')

loss_ax.legend(loc = 'upper left')
acc_ax.legend(loc = 'lower left')

png

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# callback # keras