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classification neural network python

Innocent Iguana answered on April 29, 2023 Popularity 8/10 Helpfulness 5/10

answer classification neural network python

classification neural network python

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

# Get number of predictor columns
n_cols = df.shape[1]

# One-hot encoding
y = to_categorical(y)

# define the neural network model
model = Sequential()
model.add(Dense(5, activation='relu', input_shape=(n_cols,)))
model.add(Dense(5,  activation='relu'))
model.add(Dense(1, activation='softmax'))

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

# train the model
model.fit(X_Train, y_train, epochs=50, batch_size=10, verbose=0)

# evaluate the model
loss, accuracy = model.evaluate(X_Train, y_train)
print('Categorical Crossentropy Loss:', loss)
print('Accuracy:', accuracy)

# predict
predictions = model.predict(X_Test)

Popularity 8/10 Helpfulness 5/10 Language python

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Contributed on Apr 29 2023

Innocent Iguana

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