def xor():
# Create a MLP with 2 input, a hidden layer with 2 nodes a single output node
nn = Mlp(init_nodes=2)
nn.add_layer(2)
nn.add_layer(1)
print("Training the network...")
for i in range(20000):
data = random.choice(training_xor)
nn.train(data["input"], data["output"])
# nn.save("xor.mlp")
for i in range(2):
for j in range(2):
out_class, out_prob = nn.predict([i, j])
print(
"Predicting XOR between {} and {} gave {} and the real is {} (Output: {:.2f})"
.format(i, j, out_prob > .5,
bool(i) ^ bool(j), out_prob))
def ocr(training_population=5000, testing_population=1000):
print("Loading data...")
train = pd.read_csv("../datasets/mnist_train.csv")
train = process_df(train)
test_set = pd.read_csv("../datasets/mnist_test.csv")
test_set = process_df(test_set)
print("Loaded {} rows for training.".format(train.shape[0]))
print("Loaded {} rows for testing.".format(test_set.shape[0]))
nn = Mlp(init_nodes=784, learning_rate=.05)
nn.add_layer(300)
nn.add_layer(150, function="relu")
nn.add_layer(10)
print(
"Training the network with {} samples...".format(training_population))
for i in range(training_population):
data = train.sample(n=1)
label = data["label"].tolist()[0]
inputs = list(data.iloc[0, 1:])
outputs = [0] * 10
outputs[label] = 1
nn.train(inputs, outputs)
print("Trained successfully.")
# nn.save("ocr.mlp")
print("Testing with {} samples...".format(testing_population))
c_m = np.zeros(shape=(10, 10))
for i in range(testing_population):
data = test_set.sample(n=1)
inputs = list(data.iloc[0, 1:])
label = data["label"].tolist()[0]
out_class, out_prob = nn.predict(inputs)
c_m[label][out_class] += 1
print("Results:")
correct_guesses = np.sum(np.diagonal(c_m))
total_guesses = c_m.sum()
accuracy = correct_guesses / total_guesses
recall = 0
precision = 0
c_m_t = c_m.T
for i in range(10):
correct_guesses = c_m[i][i]
total_row = np.sum(c_m[i])
total_col = np.sum(c_m_t[i])
recall += (correct_guesses / total_row) if total_row > 0 else 0
precision += (correct_guesses / total_col) if total_col > 0 else 0
recall = recall / 10
precision = precision / 10
print(
"\tRecall: {0:.2f}\n\tPrecision: {0:.2f}\n\tAccuracy: {0:.2f}".format(
recall, precision, accuracy))