loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
losses = []
accuracies = []
epochs = [1, 5, 10, 50, 100, 500, 1000]
for e in epochs:
model.fit(Xtrain, ytrain, epochs=e)
out = model.evaluate(Xtest, ytest)
losses.append(out[0])
accuracies.append(out[1])
print('\n* * * Data Summarized * * *')
for i in range(len(epochs)):
print('Epochs:', epochs[i], 'Loss:', losses[i], 'Accuracy:', accuracies[i])
df = pd.DataFrame({'epochs': epochs, 'loss': losses, 'accuracy': accuracies})
csv_name = f'./../../outputs/v1-b/activation_study/varied-epoch-{activation}-sp_out.csv'
df.to_csv(csv_name, index=False)
message = 'Subject: {}\n\n{}'.format(
'ML Model Update', f'{csv_name.split("/")[-1]} finished computing...'
f'\n\n* * * * Results * * * *\n'
f'{df}'
f'\n\nThanks! =(^_^)=')
mail(['*****@*****.**'], message)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
losses = []
accuracies = []
epochs = [1, 5, 10, 50, 100, 500, 1000]
for e in epochs:
model.fit(Xtrain, ytrain, epochs=e)
out = model.evaluate(Xtest, ytest)
losses.append(out[0])
accuracies.append(out[1])
print('\n* * * Data Summarized * * *')
for i in range(len(epochs)):
print('Epochs:', epochs[i], 'Loss:', losses[i], 'Accuracy:', accuracies[i])
df = pd.DataFrame({
'epochs': epochs,
'loss': losses,
'accuracy': accuracies
})
df.to_csv('./outputs/v1-b/varied-epoch-4HL.csv', index=False)
mail(['*****@*****.**'])