[FIXED] Keras model not compiling

Issue

I am trying to build a Keras model for a classification model and I get and error while I am trying to fit the data.

ValueError: Shapes (None, 99) and (None, 2) are incompatible

Code:

import warnings
warnings.filterwarnings(action = 'ignore')

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report

import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical

df = pd.read_csv('train.csv')

del df['ST_CASE']
df

target_column = ['MVISOBSC'] 
predictors = list(set(list(df.columns))-set(target_column))
df[predictors] = df[predictors]/df[predictors].max()

X = df[predictors].values
y = df[target_column].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
print(X_train.shape); print(X_test.shape)

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

model = Sequential()
model.add(Dense(500, activation='relu', input_dim=6))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

try:
    model.fit(X_train, y_train, epochs = 20)
except Exception as e:
    print(e)

Shape values:

X_train = (1282, 6)
X_test = (550, 6)
y_train = (1282)
y_test = (550)

I have also tried reshaping the X_train and X_test, but it does not have any effect on the error.

Solution

The no. of units in the last Dense layer must match the dimensionality of the outputs.

# Reshape the labels
y_train = np.expand_dims( y_train , axis=1 )
y_test = np.expand_dims( y_test , axis=1 )


model = Sequential()
model.add(Dense(500, activation='relu', input_dim=6))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(1, activation='softmax'))

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

Answered By - Shubham Panchal