temp.append(float(input("How much % alcohol did your drink have? \n")))
temp.append(float(input("How much ml of you drink did you have? \n")))
user_info.append(temp)
a.array(temp[3], temp[2], temp[0], hunger)
a.plot()
e = Eliminator(user_info[0][3])
e.elimination()
e.plot()
elimination_array = np.load("eliminated_array.npy")
elim_time = np.array(elimination_array[minute], ndmin=2)
bac_array.append(elimination_array[minute])
# trains neural network every 5 inputs and lets it predict the drunkeness
user_input_array.append(how_drunk)
if len(bac_array) % 5 == 0:
neural_network.learn(bac_array, user_input_array)
if len(bac_array) == 1:
neural_network.learn(bac_array, user_input_array)
prediction = neural_network.predict(elim_time)
print(prediction)
np.save("drinking_session.npy", user_info)
# informs the elimination rate adjuster on how the elimination rate will have to be adjusted
elif decision == 2:
sober_time = 0
min = int(input("How many minutes ago did you sober up? \n"))
sober_time = minute - min
e.adjustment(sober_time)
elif decision == 3:
time = input("What time is it? (enter in hh:mm format) \n")
hour = int((time[0] + time[1]))
if __name__ == "__main__":
batch, output = 10, 3
train_percent = 80
episodes = int(1e3)
learning_rate = 1e-2
encoding = [("setosa", 0), ("versicolor", 1), ("virginica", 2)]
X, y = network.extract("iris.csv",
encoding=encoding,
output=output,
label="species")
dimension = len(X[0])
neurons = [dimension, 10, 20, 20, 5, output]
trainer, tester, validater = network.partition(X, y, output, batch,
train_percent)
activity = ["relu", "relu", "relu", "relu", ""]
cost = "crossentropy"
optimizer = "adam"
model, error, accuracy = network.learn(trainer, neurons, activity,
learning_rate, episodes, cost,
optimizer)
network.plot(
error, "forestgreen",
"accumulated_errors_over_each_epoch_multilabel_classification",
"Episode", "Error")
network.plot(accuracy, "mediumvioletred",
"accuracy_over_each_epoch_multilabel_classification",
"Episode", "Learning accuracy")
network.test(model, tester, output)
import neural_network as network
if __name__ == "__main__":
X, y = network.extract("mnist", "MNIST")
Y = [element.item() for element in y]
output, window = len(list(set(Y))), 3
batch, train_percent = 32, 80
episodes, learning_rate = 10, 5e-3
trainer, tester, validater = network.partition(X, y, output, batch,
train_percent)
height, channel = X[0].shape[-1], X[0].shape[0]
kernel, stride, padding = [5, 5], [1, 1], [1, 1]
convolutions = [channel, 10, 20]
neurons = [50, output]
activity = ["relu", "relu"]
activity += ["relu", ""]
cost = "crossentropy"
optimizer = "adam"
model, error, accuracy = network.learn(trainer, neurons, activity,
learning_rate, episodes, cost,
optimizer, kernel, stride, padding,
height, window, convolutions)
network.plot(error, "forestgreen",
"accumulated_error_over_each_epoch_mnist_classification_cnn",
"Episode", "Error")
network.plot(accuracy, "mediumvioletred",
"accuracy_over_each_epoch_mnist_classification_cnn",
"Episode", "Learning accuracy")
network.test(model, tester, output, True)