Manual way to draw accuracy/loss graphs During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below: 

Epoch 1/100
691/691 [==============================] - 2174s 3s/step - loss: 0.6473 - acc: 0.6257 - val_loss: 0.5394 - val_acc: 0.8258
Epoch 2/100
691/691 [==============================] - 2145s 3s/step - loss: 0.5364 - acc: 0.7692 - val_loss: 0.4283 - val_acc: 0.8675
Epoch 3/100
691/691 [==============================] - 2124s 3s/step - loss: 0.4341 - acc: 0.8423 - val_loss: 0.3381 - val_acc: 0.9024
Epoch 4/100
691/691 [==============================] - 2126s 3s/step - loss: 0.3467 - acc: 0.8880 - val_loss: 0.2643 - val_acc: 0.9267
Epoch 5/100
691/691 [==============================] - 2123s 3s/step - loss: 0.2769 - acc: 0.9202 - val_loss: 0.2077 - val_acc: 0.9455
Epoch 6/100
691/691 [==============================] - 2118s 3s/step - loss: 0.2207 - acc: 0.9431 - val_loss: 0.1654 - val_acc: 0.9575
Epoch 7/100
691/691 [==============================] - 2125s 3s/step - loss: 0.1789 - acc: 0.9562 - val_loss: 0.1348 - val_acc: 0.9663
Epoch 8/100
691/691 [==============================] - 2120s 3s/step - loss: 0.1472 - acc: 0.9655 - val_loss: 0.1117 - val_acc: 0.9719
Epoch 9/100
691/691 [==============================] - 2119s 3s/step - loss: 0.1220 - acc: 0.9728 - val_loss: 0.0956 - val_acc: 0.9746
Epoch 10/100
691/691 [==============================] - 2119s 3s/step - loss: 0.1037 - acc: 0.9774 - val_loss: 0.0828 - val_acc: 0.9781
Epoch 11/100
691/691 [==============================] - 2110s 3s/step - loss: 0.0899 - acc: 0.9806 - val_loss: 0.0747 - val_acc: 0.9793
Epoch 12/100
691/691 [==============================] - 2123s 3s/step - loss: 0.0785 - acc: 0.9835 - val_loss: 0.0651 - val_acc: 0.9825
Epoch 13/100
691/691 [==============================] - 2130s 3s/step - loss: 0.0689 - acc: 0.9860 - val_loss: 0.0557 - val_acc: 0.9857
Epoch 14/100
691/691 [==============================] - 2124s 3s/step - loss: 0.0618 - acc: 0.9874 - val_loss: 0.0509 - val_acc: 0.9869
Epoch 15/100
691/691 [==============================] - 2122s 3s/step - loss: 0.0555 - acc: 0.9891 - val_loss: 0.0467 - val_acc: 0.9876
Epoch 16/100
152/691 [=====&gt;........................] - ETA: 22:10 - loss: 0.0515 - acc: 0.9892


My plan was to get the history variable and plot the accuracy/loss as follows:

history=model.fit_generator( .... )
plt.plot(history.history["acc"]) ...


But my training just stopped due to some hardware issues. Therefore, the graphs were not plotted. But I have the log of 15 epochs as mentioned above. Can I plot the accuracy/loss graph from the above log?
I think this covers your issue in the Keras documentation
https://keras.io/callbacks/#create-a-callback

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'))

model = Sequential()
model.add(Dense(10, input_dim=784, kernel_initializer='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

history = LossHistory()
model.fit(x_train, y_train, batch_size=128, epochs=20, verbose=0, callbacks=[history])

print(history.losses)
# outputs
'''
[0.66047596406559383, 0.3547245744908703, ..., 0.25953155204159617, 0.25901699725311789]
```