Keras weighted_metrics does not include sample weights in calculation

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I am training a CNN model with a 2D tensor of shape (400,22) as both input and output. I am using categorical_crossentropy both as loss and metric. However the loss/metrics values are very different.

My model is somewhat like this:

1. Using sample weights, and passing metrics with metrics= in model.compile.

# Imports
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.regularizers import *
from tensorflow.keras import *
import numpy as np

# Build the model
X_input = Input(shape=(400,22))
X = Conv1D(filters=32, kernel_size=2, activation='elu', 
           kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), 
           padding='same')(X_input)
X = Dropout(0.2)(X)
X = Conv1D(filters=32, kernel_size=2, activation='elu', 
           kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), 
           padding='same')(X)
X = Dropout(0.2)(X)
y = Conv1D(filters=22, kernel_size=1, activation='softmax', 
           kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), 
           padding='same')(X)
model = Model(X_input, y, name='mymodel')


# Compile and train the model (with metrics=[])
model.compile(optimizer=Adam(1e-3),
              loss=tf.keras.losses.categorical_crossentropy,
               metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.random.choice([0.01, 0.1, 1], size=20)
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)

Epoch 1/4
1/1 [==============================] - 0s 824us/step - loss: 10.2952 - categorical_crossentropy: 34.9296
Epoch 2/4
1/1 [==============================] - 0s 785us/step - loss: 10.2538 - categorical_crossentropy: 34.7858
Epoch 3/4
1/1 [==============================] - 0s 772us/step - loss: 10.2181 - categorical_crossentropy: 34.6719
Epoch 4/4
1/1 [==============================] - 0s 766us/step - loss: 10.1903 - categorical_crossentropy: 34.5797

From the results, it is evident that Keras is not using sample weights in the calculation of metrics, hence it is larger than the loss. If we change the sample weights to ones, we get the following:

2. Sample weights = ones, passing metrics with metrics= in `model.compile.

# Compile and train the model
model.compile(optimizer=Adam(1e-3),
              loss=tf.keras.losses.categorical_crossentropy,
               metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.ones((20,))
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)

Epoch 1/4
1/1 [==============================] - 0s 789us/step - loss: 35.2659 - categorical_crossentropy: 35.2573
Epoch 2/4
1/1 [==============================] - 0s 792us/step - loss: 35.0647 - categorical_crossentropy: 35.0562
Epoch 3/4
1/1 [==============================] - 0s 778us/step - loss: 34.9301 - categorical_crossentropy: 34.9216
Epoch 4/4
1/1 [==============================] - 0s 736us/step - loss: 34.8076 - categorical_crossentropy: 34.7991

Now the metrics and loss are quite close with sample weights of ones. I understand that the loss is slightly larger than metrics due to the effects of dropout, regularization, and the fact that the metric is computed at the end of each epoch, whereas the loss is the average over the batches in the training.

How can I get the metrics to include the sample weights??

3. UPDATED: using sample weights, and passing metrics with weighted_metrics= in model.compile.

It was suggested that I used weighted_metrics=[...] instead of metrics=[...] in model.compile. However, Keras still does not include the sample weights in the evaluation of the metrics.

# Compile and train the model
model.compile(optimizer=Adam(1e-3),
              loss=tf.keras.losses.categorical_crossentropy,
               weighted_metrics=[tf.keras.losses.categorical_crossentropy])
Xtrain = np.random.rand(20,400,22)
ytrain = np.random.rand(20,400,22)
np.random.seed(0)
sample_weight = np.random.choice([0.01, 0.1, 1], size=20)
history = model.fit(x=Xtrain, y=ytrain, sample_weight=sample_weight, epochs=4)

Epoch 1/4
1/1 [==============================] - 0s 764us/step - loss: 10.2581 - categorical_crossentropy: 34.9224
Epoch 2/4
1/1 [==============================] - 0s 739us/step - loss: 10.2251 - categorical_crossentropy: 34.8100
Epoch 3/4
1/1 [==============================] - 0s 755us/step - loss: 10.1854 - categorical_crossentropy: 34.6747
Epoch 4/4
1/1 [==============================] - 0s 746us/step - loss: 10.1631 - categorical_crossentropy: 34.5990

What can be done to ensure that the sample weights are evaluated in the metrics?

loss-function keras tensorflow deep-learning python
2 Answers

Keras does not automatically include sample weights in the evaluation of metrics. That's why there is a huge difference between the loss and the metrics.

If you'll like to include sample weights when evaluating metrics, pass them as weighted_metrics rather than metrics.

model.compile(optimizer=Adam(1e-3), 
              loss=tf.keras.losses.categorical_crossentropy,
              weighted_metrics=[tf.keras.losses.categorical_crossentropy]))
user_84375460

First of all, categorical cross-entropy is usually not used as a metric. Secondly, you are doing some type of seq2seq task, I hope you design the model with that intention.

Finally, in your setup, using sample_weight only works on the loss, it has no effect on the metrics or validation. There are other small bugs in your code too. Here is the fixed working code:

ref: TF 2.3.0 training keras model using tf dataset with sample weights does not apply to metrics (why sample_weight only works on loss)

import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras import *
import numpy as np

X_input = Input(shape=(400,22))
X = Conv1D(filters=32, kernel_size=2, activation='elu', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X_input)
X = Dropout(0.2)(X)
X = Conv1D(filters=32, kernel_size=2, activation='elu', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X)
X = Dropout(0.2)(X)
y = Conv1D(filters=22, kernel_size=1, activation='softmax', kernel_regularizer=L2(1e-4), bias_regularizer=L2(1e-4), padding='same')(X)

model = Model(X_input, y, name='mymodel')
model.compile(optimizer=Adam(1e-3), loss=tf.keras.losses.categorical_crossentropy,
metrics=[tf.keras.losses.categorical_crossentropy])

Xtrain = np.random.rand(10,400,22)
ytrain = np.random.rand(10,400,22)

history = model.fit(Xtrain, ytrain, sample_weight=np.ones(10), epochs=10)

Epoch 1/10
1/1 [==============================] - 1s 719ms/step - loss: 35.4521 - categorical_crossentropy: 35.4437
Epoch 2/10
1/1 [==============================] - 0s 20ms/step - loss: 35.5138 - categorical_crossentropy: 35.5054
Epoch 3/10
1/1 [==============================] - 0s 19ms/step - loss: 35.5984 - categorical_crossentropy: 35.5900
Epoch 4/10
1/1 [==============================] - 0s 19ms/step - loss: 35.6617 - categorical_crossentropy: 35.6533
Epoch 5/10
1/1 [==============================] - 0s 19ms/step - loss: 35.7807 - categorical_crossentropy: 35.7723
Epoch 6/10
1/1 [==============================] - 0s 19ms/step - loss: 35.9045 - categorical_crossentropy: 35.8961
Epoch 7/10
1/1 [==============================] - 0s 18ms/step - loss: 36.0590 - categorical_crossentropy: 36.0505
Epoch 8/10
1/1 [==============================] - 0s 19ms/step - loss: 36.2040 - categorical_crossentropy: 36.1956
Epoch 9/10
1/1 [==============================] - 0s 18ms/step - loss: 36.4169 - categorical_crossentropy: 36.4084
Epoch 10/10
1/1 [==============================] - 0s 32ms/step - loss: 36.6622 - categorical_crossentropy: 36.6538

Here, if you use no sample_weight or 1 for each sample, you will get close/similar categorical cross-entropy.

Use weighted_metrics according to docs.

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