m = Dense(32, activation='relu')(m)
op = Dense(3, activation='softmax')(m)
model = Model(input=ip, output=op)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history4 = model.fit(train_X_ex3,
train_y,
epochs=100,
batch_size=32,
verbose=0,
validation_data=(test_X_ex3, test_y))
plt.plot(history4.history['accuracy'], label='Train Accuracy')
plt.plot(history4.history['val_accuracy'], label='Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
corrects, wrongs = model.evaluate(train_X_ex3, train_y)
print("accuracy train: ", corrects / (corrects + wrongs))
corrects, wrongs = model.evaluate(test_X_ex3, test_y)
print("accuracy: test", corrects / (corrects + wrongs))
cm = model.confusion_matrix(train_X_ex3, train_y)
