def cross_validate(network_shape, epochs_num, learn_rate, _groups_x,
_groups_y):
k = _groups_x.shape[0]
_sum = 0
results = np.zeros(k)
for i in range(k):
train_x = None
train_y = None
valid_x = np.copy(
_groups_x[i]) # the validation set for th i'th iteration.
valid_y = np.copy(_groups_y[i])
net = NeuralNetwork(network_shape, epochs_num, learn_rate)
for j in range(k):
if j != i:
# arrange the train set for the i'th iteration.
if train_x is None:
train_x = np.copy(_groups_x[j])
train_y = np.copy(_groups_y[j])
else:
train_x = np.concatenate((train_x, _groups_x[j]), axis=0)
train_y = np.concatenate((train_y, _groups_y[j]), axis=0)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
net.train(train_x, train_y)
results[i] = net.accuracy(valid_x, valid_y)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
print(results)
return np.average(results)
'''
Load the data. For this demo, we're using sklearn's digits dataset
Digits are 8x8 pixel images. Each row is one image, in a linear format,
where columns 65-74 correspond to one hot encoded responses representing
digits 0 through 9. 1797 rows 74 columns
'''
data = np.loadtxt("transformed.csv", delimiter = ',')
m = len(data)
# Split the data into training set and test set.
train_set = data[:(3*m/4),:]
test_set = data[m/4:,:]
# Instantiate a new neural network. 64 input, 64 hidden, 10 output nodes.
NN = NeuralNetwork(64,HIDDEN_NODES,10,LEARNING_RATE,ITERATIONS)
# Train on the training set, test on the test set. The test() function
# will print out the percent correctness on the test set.
errors = NN.train(train_set)
NN.test(test_set)
# Plot the error curve
if VIEW_PLOT == True:
plt.plot(errors)
plt.title("Average Error Per Iteration On Training Set")
plt.xlabel("Iteration")
plt.ylabel("Average Error")
pylab.show()
if __name__ == '__main__':
test = test()
print("getting dataset")
test.getDataset(1)
print(np.asarray(test.y).reshape((9, -1)))
print(np.shape(test.X))
print(np.shape(test.y))
nn = NeuralNetwork([9, 18, 18, 9])
nn.train(X=np.asarray(test.X).reshape((9, -1)),
y=np.asarray(test.y).reshape((9, -1)),
batch_size=9,
epochs=2,
learning_rate=0.4,
print_every=10,
validation_split=0.2,
tqdm_=False,
plot_every=20000)
#X is the current gamestate and y is the next move to make
#X = np.random.random((1,9))
#print(X)
#network = Network()
#Train on the dataset
#network.train("./dataset.csv", 3)
#
# Preset Parameters "n_inputs" : image_length, # Number of input signals "n_outputs" : 1, # Number of output signals from the network "n_hidden_layers" : 1, # Number of hidden layers in the network (0 or 1 for now) "n_hiddens" : 100, # Number of nodes per hidden layer "activation_functions" : [ LReLU_function, sigmoid_function ], # Activation functions by layer # Optional parameters "weights_low" : -0.1, # Lower bound on initial weight range "weights_high" : 0.1, # Upper bound on initial weight range "save_trained_network" : False, # Save trained weights or not. "batch_size" : 1, # 1 for stochastic gradient descent, 0 for gradient descent } # Initialization network = NeuralNetwork( settings ) # Train network.train( fem_images, fem_scores, # Trainingset ERROR_LIMIT = 1e-3, # Acceptable error bounds learning_rate = 1e-5, # Learning Rate ) # Alter image network.alter_image( fem_images[0], # Image to alter fem_scores[0] # Label for initial backprop )
plt.grid(1)
plt.xlabel('epochs')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(range(history['epochs'])[:n],
history['train_acc'][:n],
label='train_acc')
plt.plot(range(history['epochs'])[:n],
history['test_acc'][:n],
label='test_acc')
plt.title('train & test accuracy')
plt.grid(1)
plt.xlabel('epochs')
plt.legend()
#LINEAR PROBLEM
data = datasets.make_blobs(n_samples=1000, centers=2, random_state=2)
X = data[0].T
y = np.expand_dims(data[1], 1).T
neural_net = NeuralNetwork([2, 4, 4, 1], seed=0)
history = neural_net.train(X=X,
y=y,
batch_size=16,
epochs=100,
learning_rate=0.4,
validation_split=0.2)
plot_history(history)
