Why is the accuracy for my Keras model always 0 when training?

Question

I\'m pretty new to keras I have built a simple network to try:

import numpy as np;

from keras.models import Sequential;
from keras.layers import Dense,Activ         


        

Answer 1

Try this one.

while trying to solve the Titanic problem from kaggle, I forgot to fill the missing data from the Dataframe, because of which the missing data was filled with "nan".

The model threw a similar output

#------------------------------------------------------

Epoch 1/50

891/891 [==============================] - 3s 3ms/step - loss: 9.8239 - acc: 0.0000e+00

Epoch 2/50

891/891 [==============================] - 1s 2ms/step - loss: 9.8231 - acc: 0.0000e+00

Epoch 3/50

891/891 [==============================] - 1s 1ms/step - loss: 9.8231 - acc: 0.0000e+00

Epoch 4/50

891/891 [==============================] - 1s 1ms/step - loss: 9.8231 - acc: 0.0000e+00

Epoch 5/50

891/891 [==============================] - 1s 1ms/step - loss: 9.8231 - acc: 0.0000e+00

#------------------------------------------------------

Make sure you prepare your data before feeding it to the model.

In my case I had to do the following changes

+++++++++++++++++++++++++++++++++++

dataset[['Age']] = dataset[['Age']].fillna(value=dataset[['Age']].mean())

dataset[['Fare']] = dataset[['Fare']].fillna(value=dataset[['Fare']].mean())

dataset[['Embarked']] = dataset[['Embarked']].fillna(value=dataset['Embarked'].value_counts().idxmax())

Answer 2

Add following to get metrics:

   history = model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_squared_error'])
   # OR
   history = model.compile(optimizer='adam', loss='mean_absolute_error', metrics=['mean_absolute_error'])
   history.history.keys()
   history.history

Answer 3

Your model seems to correspond to a regression model for the following reasons:

• You are using linear (the default one) as an activation function in the output layer (and relu in the layer before).

• Your loss is loss='mean_squared_error'.

However, the metric that you use- metrics=['accuracy'] corresponds to a classification problem. If you want to do regression, remove metrics=['accuracy']. That is, use

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

Here is a list of keras metrics for regression and classification (taken from this blog post):

Keras Regression Metrics

•Mean Squared Error: mean_squared_error, MSE or mse

•Mean Absolute Error: mean_absolute_error, MAE, mae

•Mean Absolute Percentage Error: mean_absolute_percentage_error, MAPE, mape

•Cosine Proximity: cosine_proximity, cosine

Keras Classification Metrics

•Binary Accuracy: binary_accuracy, acc

•Categorical Accuracy: categorical_accuracy, acc

•Sparse Categorical Accuracy: sparse_categorical_accuracy

•Top k Categorical Accuracy: top_k_categorical_accuracy (requires you specify a k parameter)

•Sparse Top k Categorical Accuracy: sparse_top_k_categorical_accuracy (requires you specify a k parameter)

Answer 4

I would like to point out something that is very important and has been unfortunately neglected: mean_squared_error is not an invalid loss function for classification.

The mathematical properties of cross_entropy in conjunction with the assumptions of mean_squared_error(both of which I will not expand upon in this comment) make the latter inappropriate or worse than the cross_entropy when it comes to training on classification problems.