print('Filepath: {}'.format(filepath))
print()
f = open(filepath, 'wa')
num_layers = 5
num_hidden_nodes = 784
num_training_iterations = 1000
# Get the cifar10 dataset
print("Getting data...")
(X_train, y_train), (X_test, y_test) = data.get_mnist()
# Compile network
print("Compiling model...", end='')
nn = NeuralNetwork(X_train.shape[1])
for x in range(num_layers):
nn.add_layer(Dense(num_hidden_nodes))
get_layer_for_setting(nn, setting)
nn.add_layer(Dense(10))
nn.add_layer(Activation('softmax'))
nn.compile(loss_fn='categorical_crossentropy', init_fn='lecun', pred_fn='argmax',
learning_rate=learning_rate, use_normal=True)
print('finished!')
ap_loss = []
for i in range(1, num_training_iterations+1):
start = time()
nn.train(X_train, y_train)
time_elapsed = time() - start
s = "{},{},{}".format(i, nn.get_accuracy(X_train, y_train), time_elapsed)
[6.7, 3.3, 5.7, 2.5], [6.7, 3.0, 5.2, 2.3], [6.3, 2.5, 5.0, 1.9],
[6.5, 3.0, 5.2, 2.0], [6.2, 3.4, 5.4, 2.3], [5.9, 3.0, 5.1, 1.8]])
y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
y = data.one_hot_encode(y)
ap_losses = []
relu_losses = []
for x in range(trials):
nn = NeuralNetwork(4)
for x in range(num_layers):
nn.add_layer(Dense(num_hidden_nodes))
nn.add_layer(ActivationPool(act_fns))
nn.add_layer(Dense(3))
nn.add_layer(Activation('softmax'))
nn.compile(loss_fn='categorical_crossentropy', pred_fn='argmax', learning_rate=learning_rate)
ap_losses.append(nn.get_loss(X, y))
for x in range(trials):
nn = NeuralNetwork(4)
for x in range(num_layers):
nn.add_layer(Dense(num_hidden_nodes))
nn.add_layer(Activation('relu'))
nn.add_layer(Dense(3))
nn.add_layer(Activation('softmax'))
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
y = data.one_hot_encode(y)
x_plot_before = []
y_plot_before = []
z_plot_before = []
x_plot_after = []
y_plot_after = []
z_plot_after = []
nn = NeuralNetwork(4)
for x in range(num_layers):
nn.add_layer(Dense(num_hidden_nodes))
#nn.add_layer(Activation('relu'))
nn.add_layer(MReLU(coefs=[P_val, D_val], trainable=trainable_mrelu))
nn.add_layer(Dense(3))
nn.add_layer(Activation('softmax'))
nn.compile(loss_fn='categorical_crossentropy', pred_fn='argmax', learning_rate=learning_rate)
weights = nn.layers[2].W.get_value()
for I in np.arange(0, 1, 0.05):
for J in np.arange(0, 1, 0.05):
weights[0][0] = I
weights[0][1] = J
nn.layers[2].W.set_value(weights)
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
y = data.one_hot_encode(y)
x_plot_before = []
y_plot_before = []
z_plot_before = []
x_plot_after = []
y_plot_after = []
z_plot_after = []
nn = NeuralNetwork(4)
for x in range(num_layers):
nn.add_layer(Dense(num_hidden_nodes))
#nn.add_layer(Activation('relu'))
nn.add_layer(ActivationPool([theano.tensor.nnet.relu, theano.tensor.nnet.softplus, theano.tensor.nnet.sigmoid]))
nn.add_layer(Dense(3))
nn.add_layer(Activation('softmax'))
nn.compile(loss_fn='categorical_crossentropy', pred_fn='argmax', learning_rate=learning_rate)
weights = nn.layers[2].W.get_value()
print("Final loss: {}".format(nn.get_loss(X, y)))
for I in np.arange(0, 1, 0.05):
for J in np.arange(0, 1, 0.05):
weights[0][0] = I
weights[0][1] = J
nn.layers[2].W.set_value(weights)
def test_construct_nn():
nn = NeuralNetwork(2)
nn.add_layer(Dense(40))
nn.add_layer(Activation('relu'))
nn.add_layer(Dense(2))
nn.add_layer(Activation('softmax'))
assert len(nn.layers) == 4
assert not hasattr(nn, 'train_fn')
nn.compile(categorical_crossentropy, argmax)
assert hasattr(nn, 'train_fn')
