Categorical crossentropy and label encoding - tensorflow

Question

I'm trying to code multiclass output and classes are ['A','B','C','D','E','F','G'].
Could someone elaborate more next error message:
"ValueError: You are passing a target array of shape (79, 1) while using as loss categorical_crossentropy. categorical_crossentropy expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via:
from keras.utils.np_utils import to_categorical
y_binary = to_categorical(y_int)
Alternatively, you can use the loss function sparse_categorical_crossentropy instead, which does expect integer targets."
My code:
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataa = pd.read_csv('test_out.csv')
XX = dataa.iloc[:, 0:4].values
yy = dataa.iloc[:, 4].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_Y_1 = LabelEncoder()
yy = labelencoder_Y_1.fit_transform(yy)
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(XX, yy, test_size = 0.2,
random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Part 2 - Now let's make the ANN!
# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu',
input_dim = 4))
# Adding the second hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))
# Adding the output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation =
'softmax'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy',
metrics = ['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 50)
# Part 3 - Making the predictions and evaluating the model
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

Answer 1

The problem lies in this portion of your code,
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_Y_1 = LabelEncoder()
yy = labelencoder_Y_1.fit_transform(yy)
You forgot to one-hot encode the yy, please take note that LabelEncoder only transforms your categorical data to numerical one, i.e. [A, B, C, D, E, F, G] to [1, 2, 3, 4, 5, 6, 7]. You have to one-hot encode it since you want to use softmax activation, and categorical_crossentropy (I'm over-simplifying, but it's the gist).
So, it should have been like this,
# Encoding categorical data
from keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
labelencoder_Y_1 = LabelEncoder()
yy = labelencoder_Y_1.fit_transform(yy)
yy = to_categorical(yy)

Answer 2

I assume your target class that you are going to predict is binary i.e there are only 2 possible values that could occur
If your target is binary then, the last layer of the model should be activated with sigmoid activation function. Also, the model should be compiled with binary_crossentropy or sparse_categorical_crossentropy.
If the target is multi-class i.e more than 2 possible values, you must convert the target to categorical with the help of to_categorical from keras. Then you should compile your model with categorical_crossentropy and the last layer in the model should be activated with softmax activation function.!!