def run_mlp():
# test the new way to automatically fill in inputs for models
mlp = Prototype()
x = ((None, 784), matrix("x"))
mlp.add(Dense(inputs=x, outputs=1000, activation='rectifier'))
mlp.add(Dense, outputs=1500, activation='tanh')
mlp.add(Softmax, outputs=10, out_as_probs=False)
# define our loss to optimize for the model (and the target variable)
# targets from MNIST are int64 numbers 0-9
y = lvector('y')
loss = Neg_LL(inputs=mlp.models[-1].p_y_given_x, targets=y, one_hot=False)
mnist = MNIST()
optimizer = AdaDelta(model=mlp, loss=loss, dataset=mnist, epochs=10)
optimizer.train()
test_data, test_labels = mnist.test_inputs, mnist.test_targets
test_data = test_data[:25]
test_labels = test_labels[:25]
# use the run function!
yhat = mlp.run(test_data)
print('-------')
print('Prediction: %s' % str(yhat))
print('Actual: %s' % str(test_labels.astype('int32')))
def create_mlp():
# define the model layers
relu_layer1 = Dense(input_size=784,
output_size=1000,
activation='rectifier')
relu_layer2 = Dense(inputs_hook=(1000, relu_layer1.get_outputs()),
output_size=1000,
activation='rectifier')
class_layer3 = SoftmaxLayer(inputs_hook=(1000, relu_layer2.get_outputs()),
output_size=10,
out_as_probs=False)
# add the layers as a Prototype
mlp = Prototype(layers=[relu_layer1, relu_layer2, class_layer3])
mnist = MNIST()
optimizer = AdaDelta(model=mlp, dataset=mnist, epochs=20)
optimizer.train()
test_data, test_labels = mnist.test_inputs[:25], mnist.test_targets[:25]
# use the run function!
preds = mlp.run(test_data)
log.info('-------')
log.info("predicted: %s", str(preds))
log.info("actual: %s", str(test_labels.astype('int32')))
def run_mlp():
# test the new way to automatically fill in inputs for models
mlp = Prototype()
x = ((None, 784), matrix("x"))
mlp.add(Dense(inputs=x, outputs=1000, activation='rectifier'))
mlp.add(Dense, outputs=1500, activation='tanh')
mlp.add(Softmax, outputs=10, out_as_probs=False)
# define our loss to optimize for the model (and the target variable)
# targets from MNIST are int64 numbers 0-9
y = lvector('y')
loss = Neg_LL(inputs=mlp.models[-1].p_y_given_x, targets=y, one_hot=False)
mnist = MNIST()
optimizer = AdaDelta(model=mlp, loss=loss, dataset=mnist, epochs=10)
optimizer.train()
test_data, test_labels = mnist.test_inputs, mnist.test_targets
test_data = test_data[:25]
test_labels = test_labels[:25]
# use the run function!
yhat = mlp.run(test_data)
print('-------')
print('Prediction: %s' % str(yhat))
print('Actual: %s' % str(test_labels.astype('int32')))
def create_mlp():
# define the model layers
relu_layer1 = Dense(input_size=784, output_size=1000, activation='rectifier')
relu_layer2 = Dense(inputs_hook=(1000, relu_layer1.get_outputs()), output_size=1000, activation='rectifier')
class_layer3 = SoftmaxLayer(inputs_hook=(1000, relu_layer2.get_outputs()), output_size=10, out_as_probs=False)
# add the layers as a Prototype
mlp = Prototype(layers=[relu_layer1, relu_layer2, class_layer3])
mnist = MNIST()
optimizer = AdaDelta(model=mlp, dataset=mnist, epochs=20)
optimizer.train()
test_data, test_labels = mnist.test_inputs[:25], mnist.test_targets[:25]
# use the run function!
preds = mlp.run(test_data)
log.info('-------')
log.info("predicted: %s",str(preds))
log.info("actual: %s",str(test_labels.astype('int32')))
# first need a target variable
labels = T.lvector('ys')
# negative log-likelihood for classification cost
loss = Neg_LL(inputs=lenet.models[-1].p_y_given_x,
targets=labels,
one_hot=False)
# make a monitor to view average accuracy per batch
accuracy = Monitor(name='Accuracy',
expression=1 -
(T.mean(T.neq(lenet.models[-1].y_pred, labels))),
valid=True,
test=True)
# Now grab our MNIST dataset. The version given here has each image as a single 784-dimensional vector.
# because convolutions work over 2d, let's reshape our data into the (28,28) images they originally were
# (only one channel because they are black/white images not rgb)
mnist = MNIST()
process_image = lambda img: np.reshape(img, (1, 28, 28))
mnist.train_inputs = ModifyStream(mnist.train_inputs, process_image)
mnist.valid_inputs = ModifyStream(mnist.valid_inputs, process_image)
mnist.test_inputs = ModifyStream(mnist.test_inputs, process_image)
# finally define our optimizer and train the model!
optimizer = AdaDelta(model=lenet,
dataset=mnist,
loss=loss,
epochs=10,
batch_size=64)
# train!
optimizer.train(monitor_channels=accuracy)
################
# Now that our model is complete, let's define the loss function to optimize
# first need a target variable
labels = T.lvector('ys')
# negative log-likelihood for classification cost
loss = Neg_LL(inputs=lenet.models[-1].p_y_given_x, targets=labels, one_hot=False)
# make a monitor to view average accuracy per batch
accuracy = Monitor(name='Accuracy',
expression=1-(T.mean(T.neq(lenet.models[-1].y_pred, labels))),
valid=True, test=True)
# Now grab our MNIST dataset. The version given here has each image as a single 784-dimensional vector.
# because convolutions work over 2d, let's reshape our data into the (28,28) images they originally were
# (only one channel because they are black/white images not rgb)
mnist = MNIST()
process_image = lambda img: np.reshape(img, (1, 28, 28))
mnist.train_inputs = ModifyStream(mnist.train_inputs, process_image)
mnist.valid_inputs = ModifyStream(mnist.valid_inputs, process_image)
mnist.test_inputs = ModifyStream(mnist.test_inputs, process_image)
# finally define our optimizer and train the model!
optimizer = AdaDelta(
model=lenet,
dataset=mnist,
loss=loss,
epochs=10,
batch_size=64
)
# train!
optimizer.train(monitor_channels=accuracy)
print "Creating model..."
in_shape = (None, 28*28)
in_var = matrix('xs')
mlp = Prototype()
mlp.add(Dense(inputs=(in_shape, in_var), outputs=512, activation='relu'))
mlp.add(Noise, noise='dropout', noise_level=0.5)
mlp.add(Dense, outputs=512, activation='relu')
mlp.add(Noise, noise='dropout', noise_level=0.5)
mlp.add(Softmax, outputs=10, out_as_probs=False)
print "Training..."
target_var = lvector('ys')
loss = Neg_LL(inputs=mlp.models[-1].p_y_given_x, targets=target_var, one_hot=False)
optimizer = AdaDelta(model=mlp, loss=loss, dataset=data, epochs=10)
optimizer.train()
print "Predicting..."
predictions = mlp.run(data.test_inputs)
print "Accuracy: ", float(sum(predictions==data.test_targets)) / len(data.test_targets)
# now run the dataset from kaggle
test_features = np.array(pd.read_csv("test.csv"))
predictions = mlp.run(test_features)
f = open('mlp_ predictions', 'w')
for i, digit in enumerate(predictions):
f.write(str(i+1)+","+str(digit)+"\n")
labels = T.lvector('ys')
loss = Neg_LL(inputs=lenet.models[-1].get_outputs(), targets=labels, one_hot=False)
#accuracy = Monitor(name="Accuracy", expression=1-(T.mean(T.neq(lenet.models[-1].y_pred, labels))),
# valid=True, test=True)
def greyscale_image(img):
img = img.transpose(2, 1, 0)
arr = np.average(img, 0).astype(int)
return arr[None, :, :]
def target_preprocess(img):
x, y, _ = filter_test.find_goals(img)[0]
return x/img.shape[0], y/img.shape[1]
data = ImageDataset("training_data/filtered_pics/", test_filter="**1.jpg", valid_filter="**2.jpg",
targets_preprocess=target_preprocess,
inputs_preprocess=greyscale_image)
print("Building optimizer")
optimizer = AdaDelta(model=lenet, loss=loss, dataset=data, epochs=10)
optimizer.train()
print("Predicting...")
predictions = lenet.run(data.test_inputs)
print("Accuracy: ", float(sum(predictions == data.test_targets)) / len(data.test_targets))