def main():
# loads, encodes and normalizes the dataset
X_train, y_train, X_test, y_test, N_class = load_data()
encoder = OneHotEncoder(sparse=True)
y_train = encoder.fit_transform(y_train.reshape(-1, 1)).toarray()
y_test = encoder.transform(y_test.reshape(-1, 1)).toarray()
X_train = normalize(X_train)
X_train, y_train = shuffle(X_train, y_train)
X_test = normalize(X_test)
X_test, X_validation, y_test, y_validation = train_test_split(
X_test, y_test, test_size=0.50, random_state=0)
# Convolutional neural network using 2 filters
nn = CNN(name="test",
imageWidth=100,
imageHeight=100,
hiddenSize=256,
outputSize=N_class,
filters=[(5, 5, 3, 20), (3, 3, 20, 50)],
poolSize=(2, 2),
learningRate=0.0001,
decay=0.99,
momentum=0.90)
nn.train(X_train,
y_train,
X_validation,
y_validation,
batchSize=128,
epochs=2)
count = 0
for i in range(len(X_test)):
j = nn.predictOne(X_test[i])
p = np.zeros(N_class)
p[j] = 1
if j != np.argmax(y_test[i]):
count += 1
plt.imshow(X_test[i])
plt.show()
print(encoder.inverse_transform([p]), ':',
encoder.inverse_transform([y_test[i]]))
print((len(X_test) - count) / len(X_test))
nn.close()
def main():
# loads, encodes and normalizes the dataset
X_train, y_train, X_test, y_test, N_class = load_data()
encoder = OneHotEncoder(sparse=True)
y_train = encoder.fit_transform(y_train.reshape(-1, 1)).toarray()
y_test = encoder.transform(y_test.reshape(-1, 1)).toarray()
X_train = normalize(X_train)
X_train, y_train = shuffle(X_train, y_train)
X_test = normalize(X_test)
X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.50, random_state=0)
# Convolutional neural network using 2 filters
nn = CNN(
name="test",
imageWidth=100,
imageHeight=100,
hiddenSize=256,
outputSize=N_class,
filters=[(12, 12, 3, 20), (12, 12, 20, 50)],
poolSize=(2,2),
initialization="xavier_glorot",
regularization="l2"
)
cost, accuracy = nn.train(X_train, y_train, X_validation, y_validation, batchSize=128, epochs=9)
plt.xlabel("Epochs")
plt.ylabel("Cost")
plt.plot(cost)
plt.show()
plt.xlabel("Epochs")
plt.ylabel("Validation Accuracy")
plt.plot(accuracy)
plt.show()
count=np.zeros((4,4),dtype=int)
for i in range(len(X_test)):
k = np.argmax(y_test[i])
j = nn.predictOne(X_test[i])
if j!=k:
count[k][j] += 1
#To turn on output of individual images misclassified simply change to true
if False:
p = np.zeros(N_class)
p[j] = 1
print("This picture should be classed as ",encoder.inverse_transform([y_test[i]]))
plt.imshow(X_test[i])
plt.show()
print("It has instead been classified as ",encoder.inverse_transform([p]))
for i in range(N_class):
p = np.zeros(N_class)
p[i] = 1
print(i,':',encoder.inverse_transform([p]))
print("Test phase")
print("mistakes")
print(count)
print("Test accuracy:",(len(X_test)-np.sum(count))/len(X_test))
nn.close()
