def main():
data = load_data()
target1, input1, target2, input2 = \
data['target1'], normalize(data['input1']), data['target2'], normalize(data['input2'])
## Plot some random images
plot_random_images(input2)
## End plot some random images
## 2.1
ex_2_1(input2, target2)
def main():
X_train, y_train = load_mnist('data', kind='train')
X_test, y_test = load_mnist('data', kind='t10k')
## Plot some random images
plot_random_images(X_train)
## End plot some random images
## 2.1
ex_2_1(X_train, y_train, X_test, y_test)
def main():
data = load_data()
target1, input1, target2, input2 = \
data['target1'], normalize(data['input1']), data['target2'], normalize(data['input2'])
## Plot some random images
plot_random_images(input2)
## End plot some random images
## 2.1
## ex_2_1(input2, target2)
## End 2.1
## 2.2
train_acc, test_acc, y_pred, C = ex_2_2(input1, target1, input2, target2)
plot_histogram_of_acc(train_acc, test_acc)
print(C)
def ex_2_2(input1, target1, input2, target2):
"""
Solution for exercise 2.2
:param input1: The input from dataset1
:param target1: The target from dataset1
:param input2: The input from dataset2
:param target2: The target from dataset2
:return:
"""
n = 10
train_acc = np.zeros(n)
test_acc = np.zeros(n)
pred_test = np.zeros((n, 564))
coefs = np.zeros((n, 960, 20))
#print(min(target1[:,0]), max(target1[:,0]))
# we have 20 person
for i in range(n):
classifier = MLPClassifier(hidden_layer_sizes=(20, ),
activation='tanh',
solver='adam',
max_iter=5000,
random_state=i)
classifier.fit(input1, target1[:, 0])
pred_test[i] = classifier.predict(input2)
coefs[i] = classifier.coefs_[0]
train_acc[i] = classifier.score(input1, target1[:, 0])
test_acc[i] = classifier.score(input2, target2[:, 0])
error = pred_test[1] - target2[:, 0]
for j in range(len(error)):
if (error[j] != 0):
print(j)
plot_random_images(np.row_stack((input2[175, :], input2[184, :])))
plot_random_images(np.row_stack((input2[210, :], input2[134, :])))
plot_random_images(np.row_stack((input2[223, :], input2[177, :])))
plot_random_images(np.row_stack((input2[179, :], input2[186, :])))
plot_histogram_of_acc(train_acc, test_acc)
# best network with seed i=1
confmat = confusion_matrix(target2[:, 0], pred_test[1])
print(confmat)
pass
# Compute NN weights with the best seed
best_seed = np.argmax(accu_list_train)
best_nn = nn = MLPClassifier(activation='tanh', solver='adam', max_iter=1000, hidden_layer_sizes=(n_hidden_neurons,),random_state=best_seed)
best_nn.fit(train, target_train)
# Evaluate the confusion matrix with best NN
predictions = nn.predict(test)
C = confusion_matrix(target_test, predictions)
print(C)
# Plot results
plot_histogram_of_acc(accu_list_train, accu_list_test)
print(accu_list_test)
# Find misclassified images
comp_array = target_test - predictions
comp_vector2 = np.nonzero(comp_array)
<<<<<<< HEAD
print(comp_vector2)
plot_image(test(comp_vector2(1)))
plot_image(test(comp_vector2[5]))
plot_image(test(comp_vector2[8]))
print(test(comp_vector2[1]))
=======
>>>>>>> 590cfb03b659199cfec13c3c6794c009a26452a7
# Plot misclassified image
plot_random_images(test[comp_vector2], n_images=3)
pass