def tune_network_complex_lr():
#To tune the learning rate for a complex model
print("COMPLEX MODELS: Tuning learning rate")
treshold = 0.8
accuracies = np.reshape(np.zeros(9), (3, 3))
for i in range(1, 4):
models = [
neuralNetwork([30, 200, 1],
activation="relu",
model="classifier",
learning_rate=0.05 * i),
neuralNetwork([30, 200, 1],
activation="tanh",
learning_rate=0.1,
model="classifier"),
neuralNetwork([30, 200, 1],
activation="exponential",
learning_rate=0.1,
model="classifier")
]
for j in range(3):
model = models[j]
print("------------------")
print("Learning rate: ", i * 0.05)
print("Activation function: ", model._name)
model.train(X_train,
y_train,
X_test,
y_test,
verbose=False,
plot_training_results=True,
epochs=40)
accuracy, diff = (model.accuracy_score(X_test, y_test, cutoff=0.5))
accuracies[j][i - 1] = model.best_accuracy
model.network = model.best_network
diff2 = (model.accuracy_score(X_test, y_test, cutoff=treshold))[1]
fp = len(np.ravel(np.where(diff == 1)))
fn = len(np.ravel(np.where(diff == -1)))
fp2 = len(np.ravel(np.where(diff2 == 1)))
fn2 = len(np.ravel(np.where(diff2 == -1)))
print("Accuracy: ", accuracy, "\nNumber of false positives: ", fp,
"\nNumber of false negatives: ", fn, "\nBest accuracy: ",
model.best_accuracy)
print("Confusion where y< %i is rounded down to 0:" % treshold)
print("Number of false positives: ", fp2,
"\nNumber of false negatives: ", fn2, "\nBest accuracy: ")
ax = sns.heatmap(accuracies,
xticklabels=['0.05', '0.1', '0.15'],
yticklabels=["ReLu", "tanh", "logistic"],
annot=True,
fmt=".3f")
bottom, top = ax.get_ylim()
ax.set_ylim(top - 0.5, bottom + 0.5)
plt.xlabel("Number of neurons in the hidden layer")
plt.show()
def a():
models = [
neuralNetwork([30, 60, 1],
activation="relu",
model="classifier",
learning_rate=0.1),
neuralNetwork([30, 60, 1],
activation="leakyrelu",
learning_rate=0.1,
model="classifier"),
neuralNetwork([30, 60, 1],
activation="tanh",
learning_rate=0.1,
model="classifier"),
neuralNetwork([30, 60, 1], activation="elu", model="classifier"),
neuralNetwork([30, 60, 1], activation="arctan", model="classifier"),
neuralNetwork([30, 60, 1], activation="tanh", model="classifier"),
neuralNetwork([30, 60, 1],
activation="exponential",
learning_rate=0.1,
model="classifier")
]
for model in models:
print("------------------")
print("Activation function: ", model._name)
model.train(X_train,
y_train,
X_test,
y_test,
verbose=False,
plot_training_results=True,
epochs=60)
model.network = model.best_network
accuracy, diff = (model.accuracy_score(X_test, y_test))
fp = len(np.ravel(np.where(diff == 1)))
fn = len(np.ravel(np.where(diff == -1)))
print("Accuracy: ", accuracy, "\nNumber of false positives: ", fp,
"\nNumber of false negatives: ", fn, "\nBest accuracy: ",
model.best_accuracy)
def best_network_with_cutoff():
#Cutoff for complex relu
sfp = 0
sfn = 0
print("Relu")
N = 10
for i in range(0, N):
treshold = 0.5
model = neuralNetwork([30, 200, 1],
activation="relu",
learning_rate=0.1,
model="classifier")
model.train(X_train,
y_train,
X_test,
y_test,
verbose=False,
plot_training_results=False,
epochs=20)
accuracy, diff = (model.accuracy_score(X_test, y_test, cutoff=0.5))
model.network = model.best_network
diff2 = (model.accuracy_score(X_test, y_test, cutoff=treshold))[1]
fp2 = len(np.ravel(np.where(diff2 == 1)))
fn2 = len(np.ravel(np.where(diff2 == -1)))
sfp += fp2
sfn += fn2
print("Cutoff: ", treshold)
print("Confusion where y< %i is rounded down to 0:" % treshold)
print("Number of false positives: ", fp2,
"\nNumber of false negatives: ", fn2, "\nBest accuracy: ")
#Cutoff for simple
print("Average false positives: ", sfp / float(N),
"Average false negatives: ", sfn / float(N))
sfp = 0
sfn = 0
print("\n\n\ntanh")
for i in range(0, N):
treshold = 0.5
model = neuralNetwork([30, 20, 1],
activation="tanh",
learning_rate=0.1,
model="classifier")
model.train(X_train,
y_train,
X_test,
y_test,
verbose=False,
plot_training_results=False,
epochs=20)
accuracy, diff = (model.accuracy_score(X_test, y_test, cutoff=0.5))
model.network = model.best_network
diff2 = (model.accuracy_score(X_test, y_test, cutoff=treshold))[1]
sfp += fp2
sfn += fn2
fp2 = len(np.ravel(np.where(diff2 == 1)))
fn2 = len(np.ravel(np.where(diff2 == -1)))
print("Cutoff: ", treshold)
print("Confusion where y< %i is rounded down to 0:" % treshold)
print("Number of false positives: ", fp2,
"\nNumber of false negatives: ", fn2, "\nBest accuracy: ")
print("Average false positives: ", sfp / float(N),
"Average false negatives: ", sfn / float(N))