How to interpret Keras predict output? I am new in Keras and would want to apply a neural network on this dataset: 
https://www.drivendata.org/competitions/57/nepal-earthquake/

I have proprocessed the dataset transforming categorical variables into numerical with pd.get_dummies pandas method. Also, target (that is 1, 2 or 3 - depending on the damage grade) is tranformed into three columns indicating the probability of being one of those values.

The NN I wrote is simple, but I don't understand what are the values that predict method returns. 

# first neural network with keras tutorial
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
import keras.backend as K

# define the keras model
model = Sequential()
# 12 8 3 Accuracy: 57.08
# 25 12 3 Accuracy: 56.89
model.add(Dense(25, input_dim=68, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(3, activation='softmax'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[get_f1])
# fit the keras model on the dataset
model.fit(X, y, epochs=5, batch_size=10)

predicted = model.predict(test)



This is the output for the first individual: 

array([0.09522187, 0.57914054, 0.32563758], dtype=float32)


Any idea? 

PD: If you request some additional information or code, please let me know :) 

Thanks a lot!!!
The output of softmax is a probability distribution, It gives the probability of each class of being correct. So, you have to find out 
the array index of the max value in array.

predicted_class = np.argmax(predicted)