How to plot loss terms and accuracy in keras?-CodePudding

I tried plotting the loss terms of my model, while using keras. I got the plot for 'loss' but 'val_loss' throws up a keyword error: I tried searching the internet and got this link : link to a previous post. But in this post they are using checkpoints and callbacks. While I have not implemented such features (It was not included in the tutorial I am following). Can someone help me getting around these errors! Thanks.

KeyError: 'val_loss'

Code:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np 
import math
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM


model = Sequential()

model.add(LSTM(128, input_shape=(1, step_size)))
model.add(Dropout(0.1)) # randomly select neurons to be ignored during training.

model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('linear'))

model.summary()

model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(trainX, trainY, epochs=1000, batch_size=25, verbose=2)

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

And also the same for accuracy: Note I tried changing the keyword 'acc' to 'accuracy' as mentioned in the previous post. But the same error pops up for that too: link

Error: KeyError: 'acc'

acc = history.history['acc']
val_acc = history.history['val_accuracy']
plt.plot(history.history['acc'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

CodePudding user response：

In order to plot validation data you need to have a validation data set which you do not have.You can use sklearn's train_test_split to create a validation set. Then in model.fit be sure to include validation_data(valx, valy) and set the validation batch_size. The code below makes a very nice plot of the train and validation model performance

def tr_plot(tr_data, start_epoch):
    #Plot the training and validation data
    tacc=tr_data.history['accuracy']
    tloss=tr_data.history['loss']
    vacc=tr_data.history['val_accuracy']
    vloss=tr_data.history['val_loss']
    Epoch_count=len(tacc)  start_epoch
    Epochs=[]
    for i in range (start_epoch ,Epoch_count):
        Epochs.append(i 1)   
    index_loss=np.argmin(vloss)#  this is the epoch with the lowest validation loss
    val_lowest=vloss[index_loss]
    index_acc=np.argmax(vacc)
    acc_highest=vacc[index_acc]
    plt.style.use('fivethirtyeight')
    sc_label='best epoch= '  str(index_loss 1  start_epoch)
    vc_label='best epoch= '  str(index_acc   1  start_epoch)
    fig,axes=plt.subplots(nrows=1, ncols=2, figsize=(25,10))
    axes[0].plot(Epochs,tloss, 'r', label='Training loss')
    axes[0].plot(Epochs,vloss,'g',label='Validation loss' )
    axes[0].scatter(index_loss 1  start_epoch,val_lowest, s=150, c= 'blue', label=sc_label)
    axes[0].scatter(Epochs, tloss, s=100, c='red')    
    axes[0].set_title('Training and Validation Loss')
    axes[0].set_xlabel('Epochs', fontsize=18)
    axes[0].set_ylabel('Loss', fontsize=18)
    axes[0].legend()
    axes[1].plot (Epochs,tacc,'r',label= 'Training Accuracy')
    axes[1].scatter(Epochs, tacc, s=100, c='red')
    axes[1].plot (Epochs,vacc,'g',label= 'Validation Accuracy')
    axes[1].scatter(index_acc 1  start_epoch,acc_highest, s=150, c= 'blue', label=vc_label)
    axes[1].set_title('Training and Validation Accuracy')
    axes[1].set_xlabel('Epochs', fontsize=18)
    axes[1].set_ylabel('Accuracy', fontsize=18)
    axes[1].legend()
    plt.tight_layout    
    plt.show()
    return index_loss
    
loss_index=tr_plot(history,0)

CodePudding user response：

import seaborn as sns
sns.set_style('darkgrid')
def tr_plot(tr_data, start_epoch):
    #Plot the training and validation data
    tacc=tr_data.history['accuracy']
    tloss=tr_data.history['loss']
    vacc=tr_data.history['val_accuracy']
    vloss=tr_data.history['val_loss']
    Epoch_count=len(tacc)  start_epoch
    Epochs=[]
    for i in range (start_epoch ,Epoch_count):
        Epochs.append(i 1)   
    index_loss=np.argmin(vloss)#  this is the epoch with the lowest validation loss
    val_lowest=vloss[index_loss]
    index_acc=np.argmax(vacc)
    acc_highest=vacc[index_acc]
    plt.style.use('fivethirtyeight')
    sc_label='best epoch= '  str(index_loss 1  start_epoch)
    vc_label='best epoch= '  str(index_acc   1  start_epoch)
    fig,axes=plt.subplots(nrows=1, ncols=2, figsize=(25,10))
    axes[0].plot(Epochs,tloss, 'r', label='Training loss')
    axes[0].plot(Epochs,vloss,'g',label='Validation loss' )
    axes[0].scatter(index_loss 1  start_epoch,val_lowest, s=150, c= 'blue', label=sc_label)
    axes[0].scatter(Epochs, tloss, s=100, c='red')    
    axes[0].set_title('Training and Validation Loss')
    axes[0].set_xlabel('Epochs', fontsize=18)
    axes[0].set_ylabel('Loss', fontsize=18)
    axes[0].legend()
    axes[1].plot (Epochs,tacc,'r',label= 'Training Accuracy')
    axes[1].scatter(Epochs, tacc, s=100, c='red')
    axes[1].plot (Epochs,vacc,'g',label= 'Validation Accuracy')
    axes[1].scatter(index_acc 1  start_epoch,acc_highest, s=150, c= 'blue', label=vc_label)
    axes[1].set_title('Training and Validation Accuracy')
    axes[1].set_xlabel('Epochs', fontsize=18)
    axes[1].set_ylabel('Accuracy', fontsize=18)
    axes[1].legend()
    plt.tight_layout    
    plt.show()
    return index_loss
    
loss_index=tr_plot(history,0)