def __init__(self,
input_dim=3 * 32 * 32,
hidden_dim=100,
num_classes=10,
weight_scale=1e-3,
reg=0.0,
conv_mode='lazy',
dtype=py_np.float64):
"""
Initialize a new network.
Inputs:
- input_dim: An integer giving the size of the input
- hidden_dim: An integer giving the size of the hidden layer
- num_classes: An integer giving the number of classes to classify
- dropout: Scalar between 0 and 1 giving dropout strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- reg: Scalar giving L2 regularization strength.
"""
super(TwoLayerNet, self).__init__(conv_mode)
self.params = {}
self.reg = reg
self.params['W1'] = random.randn(input_dim, hidden_dim) * weight_scale
self.params['b1'] = np.zeros((hidden_dim))
self.params['W2'] = random.randn(hidden_dim, num_classes) * weight_scale
self.params['b2'] = np.zeros((num_classes))
def __init__(self,
input_dim=3 * 32 * 32,
hidden_dim=100,
num_classes=10,
weight_scale=1e-3,
reg=0.0,
conv_mode='lazy',
dtype=py_np.float64):
"""
Initialize a new network.
Inputs:
- input_dim: An integer giving the size of the input
- hidden_dim: An integer giving the size of the hidden layer
- num_classes: An integer giving the number of classes to classify
- dropout: Scalar between 0 and 1 giving dropout strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- reg: Scalar giving L2 regularization strength.
"""
super(TwoLayerNet, self).__init__(conv_mode)
self.params = {}
self.reg = reg
self.params['W1'] = random.randn(input_dim, hidden_dim) * weight_scale
self.params['b1'] = np.zeros((hidden_dim))
self.params['W2'] = random.randn(hidden_dim,
num_classes) * weight_scale
self.params['b2'] = np.zeros((num_classes))
def xavier(shape, _):
"""Initialize weights with xavier initializer.
Xavier initializer init matrix based on fan_in and fan_out
Parameters
----------
shape : tuple
Shape of the array to be initialized.
_ : placeholder
Returns
-------
Array
Initialized array of size `shape`.
"""
fan_out = shape[0]
if len(shape) > 1:
fan_in = numpy.prod(shape[1:])
else:
fan_in = 0
var = numpy.sqrt(6.0 / (fan_out + fan_in))
ret = npr.randn(*shape) * var
return ret
def xavier(shape, config):
fan_out = shape[0]
if len(shape) > 1:
fan_in = numpy.prod(shape[1:])
else:
fan_in = 0
var = numpy.sqrt(6.0 / (fan_out + fan_in))
ret = npr.randn(*shape) * var
return ret
def set_param(self):
self.params = {}
c_cnt, height, width = self.input_dim
f_cnt = self.num_filters
f_h, f_w = self.filter_size, self.filter_size
self.params['conv1_weight'] = random.randn(f_cnt, c_cnt, f_h,
f_w) * self.weight_scale
self.params['conv1_bias'] = np.zeros(f_cnt)
#TODO(Haoran): whole stuff about all dimension calculations
#should be substituted by quering symbol.arg_list
conv_stride = 1
conv_pad = (f_h - 1) / 2
Hc, Wc = 1 + (height + 2 * conv_pad - f_h) / conv_stride, 1 + (
width + 2 * conv_pad - f_w) / conv_stride
pool_height, pool_width = 2, 2
pool_stride = 2
Hp, Wp = (Hc - pool_height) / pool_stride + 1, (
Wc - pool_width) / pool_stride + 1
# weight has to be tranposed to fit mxnet's symbol
self.params['fc1_weight'] = np.transpose(
random.randn(5408, self.hidden_dim) * self.weight_scale)
self.params['fc1_bias'] = np.zeros((self.hidden_dim))
# weight has to be tranposed to fit mxnet's symbol
self.params['fc2_weight'] = np.transpose(
random.randn(self.hidden_dim, self.num_classes) *
self.weight_scale)
self.params['fc2_bias'] = np.zeros((self.num_classes))
#TODO(Haoran): move following into parent structured model class
self.param_keys = self.params.keys()
# Build key's index in loss func's arglist
self.key_args_index = {}
for i, key in enumerate(self.param_keys):
# data, targets would be the first two elments in arglist
self.key_args_index[key] = self.data_target_cnt + i
def set_param(self):
self.params = {}
c_cnt, height, width = self.input_dim
f_cnt = self.num_filters
f_h, f_w = self.filter_size, self.filter_size
self.params['conv1_weight'] = random.randn(f_cnt, c_cnt, f_h,
f_w) * self.weight_scale
self.params['conv1_bias'] = np.zeros(f_cnt)
#TODO(Haoran): whole stuff about all dimension calculations
#should be substituted by quering symbol.arg_list
conv_stride = 1
conv_pad = (f_h - 1) / 2
Hc, Wc = 1 + (height + 2 * conv_pad - f_h) / conv_stride, 1 + (
width + 2 * conv_pad - f_w) / conv_stride
pool_height, pool_width = 2, 2
pool_stride = 2
Hp, Wp = (Hc - pool_height) / pool_stride + 1, (Wc - pool_width
) / pool_stride + 1
# weight has to be tranposed to fit mxnet's symbol
self.params['fc1_weight'] = np.transpose(random.randn(
5408, self.hidden_dim) * self.weight_scale)
self.params['fc1_bias'] = np.zeros((self.hidden_dim))
# weight has to be tranposed to fit mxnet's symbol
self.params['fc2_weight'] = np.transpose(random.randn(
self.hidden_dim, self.num_classes) * self.weight_scale)
self.params['fc2_bias'] = np.zeros((self.num_classes))
#TODO(Haoran): move following into parent structured model class
self.param_keys = self.params.keys()
# Build key's index in loss func's arglist
self.key_args_index = {}
for i, key in enumerate(self.param_keys):
# data, targets would be the first two elments in arglist
self.key_args_index[key] = self.data_target_cnt + i
def test_policy():
@minpy.wrap_policy(minpy.OnlyNumPyPolicy())
def gaussian_cluster_generator(num_samples=10000,
num_features=500,
num_classes=5):
# with minpy.OnlyNumPyPolicy():
mu = np.random.rand(num_classes, num_features)
sigma = np.ones((num_classes, num_features)) * 0.1
num_cls_samples = num_samples / num_classes
x = np.zeros((num_samples, num_features))
y = np.zeros((num_samples, num_classes))
for i in range(num_classes):
cls_samples = np.random.normal(mu[i, :], sigma[i, :],
(num_cls_samples, num_features))
x[i * num_cls_samples:(i + 1) * num_cls_samples] = cls_samples
y[i * num_cls_samples:(i + 1) * num_cls_samples, i] = 1
return x, y
def predict(w, x):
a = np.exp(np.dot(x, w))
a_sum = np.sum(a, axis=1, keepdims=True)
prob = a / a_sum
return prob
def train_loss(w, x):
prob = predict(w, x)
loss = -np.sum(label * np.log(prob)) / num_samples
return loss
"""Use Minpy's auto-grad to derive a gradient function off loss"""
grad_function = grad_and_loss(train_loss)
# Using gradient descent to fit the correct classes.
def train(w, x, loops):
for i in range(loops):
dw, loss = grad_function(w, x)
if i % 10 == 0:
print('Iter {}, training loss {}'.format(i, loss))
# gradient descent
w -= 0.1 * dw
# Initialize training data.
num_samples = 10000
num_features = 500
num_classes = 5
data, label = gaussian_cluster_generator(num_samples, num_features,
num_classes)
# Initialize training weight and train
weight = random.randn(num_features, num_classes)
train(weight, data, 100)
def test_policy():
@minpy.wrap_policy(minpy.OnlyNumPyPolicy())
def gaussian_cluster_generator(num_samples=10000, num_features=500, num_classes=5):
# with minpy.OnlyNumPyPolicy():
mu = np.random.rand(num_classes, num_features)
sigma = np.ones((num_classes, num_features)) * 0.1
num_cls_samples = num_samples / num_classes
x = np.zeros((num_samples, num_features))
y = np.zeros((num_samples, num_classes))
for i in range(num_classes):
cls_samples = np.random.normal(mu[i,:], sigma[i,:], (num_cls_samples, num_features))
x[i*num_cls_samples:(i+1)*num_cls_samples] = cls_samples
y[i*num_cls_samples:(i+1)*num_cls_samples,i] = 1
return x, y
def predict(w, x):
a = np.exp(np.dot(x, w))
a_sum = np.sum(a, axis=1, keepdims=True)
prob = a / a_sum
return prob
def train_loss(w, x):
prob = predict(w, x)
loss = -np.sum(label * np.log(prob)) / num_samples
return loss
"""Use Minpy's auto-grad to derive a gradient function off loss"""
grad_function = grad_and_loss(train_loss)
# Using gradient descent to fit the correct classes.
def train(w, x, loops):
for i in range(loops):
dw, loss = grad_function(w, x)
if i % 10 == 0:
print('Iter {}, training loss {}'.format(i, loss))
# gradient descent
w -= 0.1 * dw
# Initialize training data.
num_samples = 10000
num_features = 500
num_classes = 5
data, label = gaussian_cluster_generator(num_samples, num_features, num_classes)
# Initialize training weight and train
weight = random.randn(num_features, num_classes)
train(weight, data, 100)
def gaussian(shape, config):
"""Initialize weights with gaussian distribution.
Parameters
----------
shape : tuple
Shape of the array to be initialized.
config : dict
Mean and standard variance of the distribution
Returns
-------
Array
Initialized array of size `shape`
"""
config.setdefault('mu', 0.0)
config.setdefault('stdvar', 0.001)
stdvar = config['stdvar']
meanvar = config['mu']
return npr.randn(*shape) * stdvar + meanvar
def gaussian(shape, config):
"""Initialize weights with gaussian distribution.
Parameters
----------
shape : tuple
Shape of the array to be initialized.
config : dict
Mean and standard variance of the distribution
Returns
-------
Array
Initialized array of size `shape`
"""
config.setdefault('mu', 0.0)
config.setdefault('stdvar', 0.001)
stdvar = config['stdvar']
meanvar = config['mu']
return npr.randn(*shape) * stdvar + meanvar
def gaussian(shape, config):
config.setdefault('mu', 0.0)
config.setdefault('stdvar', 0.001)
stdvar = config['stdvar']
mu = config['mu']
return npr.randn(*shape) * stdvar + mu
train_inputs = build_dataset(__file__, seq_length, input_size, max_lines=60)
pred_fun, loglike_fun, num_weights = build_lstm(input_size, state_size, output_size)
def print_training_prediction(weights):
print("Training text Predicted text")
logprobs = np.asarray(pred_fun(weights, train_inputs))
for t in range(logprobs.shape[1]):
training_text = one_hot_to_string(train_inputs[:,t,:])
predicted_text = one_hot_to_string(logprobs[:,t,:])
print(training_text.replace('\n', ' ') + "|" + predicted_text.replace('\n', ' '))
# Wrap function to only have one argument, for scipy.minimize.
def training_loss(weights):
return -loglike_fun(weights, train_inputs, train_inputs)
def callback(weights):
print("Train loss:", training_loss(weights))
print_training_prediction(weights)
# Build gradient of loss function using autograd.
training_loss_and_grad = value_and_grad(training_loss)
init_weights = npr.randn(num_weights) * param_scale
print("Training LSTM...")
result = minimize(training_loss_and_grad, init_weights, jac=True, method='CG',
options={'maxiter':train_iters}, callback=callback)
trained_weights = result.x
def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10,
dropout=0, use_batchnorm=False, reg=0.0,
weight_scale=1e-2, dtype=np.float32, seed=None):
"""
Initialize a new FullyConnectedNet.
Inputs:
- hidden_dims: A list of integers giving the size of each hidden layer.
- input_dim: An integer giving the size of the input.
- num_classes: An integer giving the number of classes to classify.
- dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
the network should not use dropout at all.
- use_batchnorm: Whether or not the network should use batch normalization.
- reg: Scalar giving L2 regularization strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- dtype: A numpy datatype object; all computations will be performed using
this datatype. float32 is faster but less accurate, so you should use
float64 for numeric gradient checking.
- seed: If not None, then pass this random seed to the dropout layers. This
will make the dropout layers deteriminstic so we can gradient check the
model.
"""
self.use_batchnorm = use_batchnorm
self.use_dropout = dropout > 0
self.reg = reg
self.num_layers = 1 + len(hidden_dims)
self.dtype = dtype
self.params = {}
############################################################################
# TODO: Initialize the parameters of the network, storing all values in #
# the self.params dictionary. Store weights and biases for the first layer #
# in W1 and b1; for the second layer use W2 and b2, etc. Weights should be #
# initialized from a normal distribution with standard deviation equal to #
# weight_scale and biases should be initialized to zero. #
# #
# When using batch normalization, store scale and shift parameters for the #
# first layer in gamma1 and beta1; for the second layer use gamma2 and #
# beta2, etc. Scale parameters should be initialized to one and shift #
# parameters should be initialized to zero. #
############################################################################
for l in xrange(self.num_layers):
if l == 0:
input_d = input_dim
else:
input_d = hidden_dims[l-1]
if l < self.num_layers - 1:
out_d = hidden_dims[l]
else:
out_d = num_classes
self.params[self.GetWeightName(l)] = random.randn(input_d, out_d) * weight_scale
self.params[self.GetBiasName(l)] = np.zeros((out_d))
############################################################################
# END OF YOUR CODE #
############################################################################
# When using dropout we need to pass a dropout_param dictionary to each
# dropout layer so that the layer knows the dropout probability and the mode
# (train / test). You can pass the same dropout_param to each dropout layer.
self.dropout_param = {}
if self.use_dropout:
self.dropout_param = {'mode': 'train', 'p': dropout}
if seed is not None:
self.dropout_param['seed'] = seed
# With batch normalization we need to keep track of running means and
# variances, so we need to pass a special bn_param object to each batch
# normalization layer. You should pass self.bn_params[0] to the forward pass
# of the first batch normalization layer, self.bn_params[1] to the forward
# pass of the second batch normalization layer, etc.
self.bn_params = []
if self.use_batchnorm:
self.bn_params = [{'mode': 'train'} for i in xrange(self.num_layers - 1)]
# Cast all parameters to the correct datatype
for k, v in self.params.iteritems():
self.params[k] = v.astype(dtype)
def test_stack():
arr = [rnd.randn(3, 4) for _ in range(10)]
res = np.stack(arr)
assert res.shape == (10, 3, 4)
def test_concatenate():
arr = [rnd.randn(3, 4) for _ in range(10)]
res = np.concatenate(arr, axis=1)
assert res.shape == (3, 40)
def xavier(shape, config=None):
var = len(shape) / sum(shape)
return npr.randn(*shape) * var
def _randn(l, c):
return random.randn(l, c)
logprobs = np.asarray(pred_fun(weights, train_inputs))
for t in range(logprobs.shape[1]):
training_text = one_hot_to_string(train_inputs[:, t, :])
predicted_text = one_hot_to_string(logprobs[:, t, :])
print(
training_text.replace('\n', ' ') + "|" +
predicted_text.replace('\n', ' '))
# Wrap function to only have one argument, for scipy.minimize.
def training_loss(weights):
return -loglike_fun(weights, train_inputs, train_inputs)
def callback(weights):
print("Train loss:", training_loss(weights))
print_training_prediction(weights)
# Build gradient of loss function using autograd.
training_loss_and_grad = value_and_grad(training_loss)
init_weights = npr.randn(num_weights) * param_scale
print("Training LSTM...")
result = minimize(training_loss_and_grad,
init_weights,
jac=True,
method='CG',
options={'maxiter': train_iters},
callback=callback)
trained_weights = result.x
def __init__(self,
hidden_dims,
input_dim=3 * 32 * 32,
num_classes=10,
dropout=0,
use_batchnorm=False,
reg=0.0,
weight_scale=1e-2,
seed=None,
dtype=py_np.float64,
conv_mode='lazy'):
"""
Initialize a new FullyConnectedNet.
Inputs:
- hidden_dims: A list of integers giving the size of each hidden layer.
- input_dim: An integer giving the size of the input.
- num_classes: An integer giving the number of classes to classify.
- dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
the network should not use dropout at all.
- use_batchnorm: Whether or not the network should use batch normalization.
- reg: Scalar giving L2 regularization strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- seed: If not None, then pass this random seed to the dropout layers. This
will make the dropout layers deteriminstic so we can gradient check the
model.
"""
super(FullyConnectedNet, self).__init__(conv_mode)
self.use_batchnorm = use_batchnorm
self.use_dropout = dropout > 0
self.reg = reg
self.num_layers = 1 + len(hidden_dims)
self.params = {}
#Define parameter name given # layer
self.w_name = lambda l: 'W' + str(l)
self.b_name = lambda l: 'b' + str(l)
self.bn_ga_name = lambda l: 'bn_ga' + str(l)
self.bn_bt_name = lambda l: 'bn_bt' + str(l)
for l in range(self.num_layers):
if l == 0:
input_d = input_dim
else:
input_d = hidden_dims[l - 1]
if l < self.num_layers - 1:
out_d = hidden_dims[l]
else:
out_d = num_classes
self.params[self.w_name(l)] = random.randn(input_d,
out_d) * weight_scale
self.params[self.b_name(l)] = np.zeros((out_d))
if l < self.num_layers and self.use_batchnorm:
self.params[self.bn_ga_name(l)] = np.ones((out_d))
self.params[self.bn_bt_name(l)] = np.zeros((out_d))
self.param_keys = self.params.keys()
# When using dropout we need to pass a dropout_param dictionary to each
# dropout layer so that the layer knows the dropout probability and the mode
# (train / test). You can pass the same dropout_param to each dropout layer.
self.dropout_param = {}
if self.use_dropout:
self.dropout_param = {'mode': 'train', 'p': dropout}
if seed is not None:
self.dropout_param['seed'] = seed
# With batch normalization we need to keep track of running means and
# variances, so we need to pass a special bn_param object to each batch
# normalization layer.
self.bn_params = []
if self.use_batchnorm:
self.bn_params = [{
'mode': 'train'
} for i in xrange(self.num_layers - 1)]
# Build key's index in loss func's arglist
self.key_args_index = {}
for i, key in enumerate(self.param_keys):
# data, targets would be the first two elments in arglist
self.key_args_index[key] = self.data_target_cnt + i
# Init Key to index in loss_function args
self.w_idx = self.wrap_param_idx(self.w_name)
self.b_idx = self.wrap_param_idx(self.b_name)
self.bn_ga_idx = self.wrap_param_idx(self.bn_ga_name)
self.bn_bt_idx = self.wrap_param_idx(self.bn_bt_name)
def train_loss(w, x):
prob = predict(w, x)
loss = -np.sum(label * np.log(prob)) / num_samples
return loss
"""Use Minpy's auto-grad to derive a gradient function off loss"""
grad_function = grad_and_loss(train_loss)
# Using gradient descent to fit the correct classes.
def train(w, x, loops):
for i in range(loops):
dw, loss = grad_function(w, x)
if i % 10 == 0:
print('Iter {}, training loss {}'.format(i, loss))
# gradient descent
w -= 0.1 * dw
# Initialize training data.
num_samples = 10000
num_features = 500
num_classes = 5
data, label = make_data(num_samples, num_features, num_classes)
# Initialize training weight and train
weight = random.randn(num_features, num_classes)
train(weight, data, 100)
def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10,
dropout=0, use_batchnorm=False, reg=0.0,
weight_scale=1e-2, seed=None, dtype=py_np.float64, conv_mode='lazy'):
"""
Initialize a new FullyConnectedNet.
Inputs:
- hidden_dims: A list of integers giving the size of each hidden layer.
- input_dim: An integer giving the size of the input.
- num_classes: An integer giving the number of classes to classify.
- dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
the network should not use dropout at all.
- use_batchnorm: Whether or not the network should use batch normalization.
- reg: Scalar giving L2 regularization strength.
- weight_scale: Scalar giving the standard deviation for random
initialization of the weights.
- seed: If not None, then pass this random seed to the dropout layers. This
will make the dropout layers deteriminstic so we can gradient check the
model.
"""
super(FullyConnectedNet, self).__init__(conv_mode)
self.use_batchnorm = use_batchnorm
self.use_dropout = dropout > 0
self.reg = reg
self.num_layers = 1 + len(hidden_dims)
self.params = {}
#Define parameter name given # layer
self.w_name = lambda l: 'W' + str(l)
self.b_name = lambda l: 'b' + str(l)
self.bn_ga_name = lambda l: 'bn_ga' + str(l)
self.bn_bt_name = lambda l: 'bn_bt' + str(l)
for l in range(self.num_layers):
if l == 0:
input_d = input_dim
else:
input_d = hidden_dims[l-1]
if l < self.num_layers - 1:
out_d = hidden_dims[l]
else:
out_d = num_classes
self.params[self.w_name(l)] = random.randn(input_d, out_d) * weight_scale
self.params[self.b_name(l)] = np.zeros((out_d))
if l < self.num_layers and self.use_batchnorm:
self.params[self.bn_ga_name(l)] = np.ones((out_d))
self.params[self.bn_bt_name(l)] = np.zeros((out_d))
self.param_keys = self.params.keys()
# When using dropout we need to pass a dropout_param dictionary to each
# dropout layer so that the layer knows the dropout probability and the mode
# (train / test). You can pass the same dropout_param to each dropout layer.
self.dropout_param = {}
if self.use_dropout:
self.dropout_param = {'mode': 'train', 'p': dropout}
if seed is not None:
self.dropout_param['seed'] = seed
# With batch normalization we need to keep track of running means and
# variances, so we need to pass a special bn_param object to each batch
# normalization layer.
self.bn_params = []
if self.use_batchnorm:
self.bn_params = [{'mode': 'train'} for i in xrange(self.num_layers - 1)]
# Build key's index in loss func's arglist
self.key_args_index = {}
for i, key in enumerate(self.param_keys):
# data, targets would be the first two elments in arglist
self.key_args_index[key] = self.data_target_cnt + i
# Init Key to index in loss_function args
self.w_idx = self.wrap_param_idx(self.w_name)
self.b_idx = self.wrap_param_idx(self.b_name)
self.bn_ga_idx = self.wrap_param_idx(self.bn_ga_name)
self.bn_bt_idx = self.wrap_param_idx(self.bn_bt_name)
a_sum = np.sum(a, axis=1, keepdims=True)
prob = a / a_sum
return prob
def train_loss(w, x):
prob = predict(w, x)
loss = -np.sum(label * np.log(prob)) / num_samples
return loss
"""Use Minpy's auto-grad to derive a gradient function off loss"""
grad_function = grad_and_loss(train_loss)
# Using gradient descent to fit the correct classes.
def train(w, x, loops):
for i in range(loops):
dw, loss = grad_function(w, x)
if i % 10 == 0:
print('Iter {}, training loss {}'.format(i, loss))
# gradient descent
w -= 0.1 * dw
# Initialize training data.
num_samples = 10000
num_features = 500
num_classes = 5
data, label = make_data(num_samples, num_features, num_classes)
# Initialize training weight and train
weight = random.randn(num_features, num_classes)
train(weight, data, 100)
