Get loss values for each training instance - Keras

Question

I want to get loss values as model train with each instance.

history = model.fit(..)

for example above code returns the loss values for each epoch not mini batch or instance.

what is the best way to do this? Any suggestions?

Answer 1

There is exactly what you are looking for at the end of this official keras documentation page https://keras.io/callbacks/#callback

Here is the code to create a custom 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]
'''

Answer 2

If you want to get loss values for each batch, you might want to use call model.train_on_batch inside a generator. It's hard to provide a complete example without knowing your dataset, but you will have to break your dataset into batches and feed them one by one

def make_batches(...):
    ...

batches = make_batches(...)
batch_losses = [model.train_on_batch(x, y) for x, y in batches]

It's a bit more complicated with single instances. You can, of course, train on 1-sized batches, though it will most likely thrash your optimiser (by maximising gradient variance) and significantly degrade performance. Besides, since loss functions are evaluated outside of Python's domain, there is no direct way to hijack the computation without tinkering with C/C++ and CUDA sources. Even then, the backend itself evaluates the loss batch-wise (benefitting from highly vectorised matrix-operations), therefore you will severely degrade performance by forcing it to evaluate loss on each instance. Simply put, hacking the backend will only (probably) help you reduce GPU memory transfers (as compared to training on 1-sized batches from the Python interface). If you really want to get per-instance scores, I would recommend you to train on batches and evaluate on instances (this way you will avoid issues with high variance and reduce expensive gradient computations, since gradients are only estimated during training):

def make_batches(batchsize, x, y):
    ...


batchsize = n
batches = make_batches(n, ...)
batch_instances = [make_batches(1, x, y) for x, y in batches]
losses = [
    (model.train_on_batch(x, y), [model.test_on_batch(*inst) for inst in instances]) 
    for batch, instances in zip(batches, batch_instances)
]

Answer 3

After combining resources from here and here I came up with the following code. Maybe it will help you. The idea is that you can override the Callbacks class from keras and then use the on_batch_end method to check the loss value from the logs that keras will supply automatically to that method.

Here is a working code of an NN with that particular function built in. Maybe you can start from here -

import numpy as np
import pandas as pd
import seaborn as sns
import os
import matplotlib.pyplot as plt
import time
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import Callback

# fix random seed for reproducibility
seed = 155
np.random.seed(seed)

# load pima indians dataset

# download directly from website
dataset = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data", 
                      header=None).values
X_train, X_test, Y_train, Y_test = train_test_split(dataset[:,0:8], dataset[:,8], test_size=0.25, random_state=87)
class NBatchLogger(Callback):
    def __init__(self,display=100):
        '''
        display: Number of batches to wait before outputting loss
        '''
        self.seen = 0
        self.display = display

    def on_batch_end(self,batch,logs={}):
        self.seen += logs.get('size', 0)
        if self.seen % self.display == 0:
            print('\n{0}/{1} - Batch Loss: {2}'.format(self.seen,self.params['samples'],
                                                logs.get('loss')))


out_batch = NBatchLogger(display=1000)
np.random.seed(seed)
my_first_nn = Sequential() # create model
my_first_nn.add(Dense(5, input_dim=8, activation='relu')) # hidden layer
my_first_nn.add(Dense(1, activation='sigmoid')) # output layer
my_first_nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

my_first_nn_fitted = my_first_nn.fit(X_train, Y_train, epochs=1000, verbose=0, batch_size=128,
                                     callbacks=[out_batch], initial_epoch=0)

Please let me know if you wanted to have something like this.

Answer 4

One solution is to calculate the loss function between train expectations and predictions from train input. In the case of loss = mean_squared_error and three dimensional outputs (ie image width x height x channels):

model.fit(train_in,train_out,...)

pred = model.predict(train_in)
loss = np.add.reduce(np.square(test_out-pred),axis=(1,2,3)) # this computes the total squared error for each sample
loss = loss / ( pred.shape[1]*pred.shape[2]*pred.shape[3]) # this computes the mean over the sample entry 

np.savetxt("loss.txt",loss) # This line saves the data to file