def _quant_var(
name,
initializer_val,
vars_collection=tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
):
"""Create an var for storing the min/max quantization range."""
return contrib_framework.model_variable(
name,
shape=[],
initializer=tf.constant_initializer(initializer_val),
collections=[vars_collection],
trainable=False)
def create_latent_space(batch_size, shape, steps=None):
""" Create the latent space """
# Setup the latent space. The latent space is a 2-D tensor used for each element in the batch
# with dimensions [batch_size, latent_size, latent_size]. If steps are provided then there is a
# latent space per step with dimensions [step, batch_size, latent_size, latent_size].
latent_shape = shape
if steps is not None:
latent_shape = (steps,) + latent_shape
latent_space = framework.model_variable(
'LatentSpace', shape=latent_shape, trainable=True,
initializer=tf.random_uniform_initializer(0.0, 1e-3))
latent_space = tf.tile(latent_space, (batch_size,) + (1,) * (len(latent_shape) - 1))
latent_space = tf.reshape(latent_space, (batch_size,) + latent_shape)
if steps is not None:
permutation = (1, 0) + tuple(x + 2 for x in range(len(shape)))
latent_space = tf.transpose(latent_space, permutation)
return latent_space
def spatial_conv(inputs,
conv_type,
kernel,
filters,
stride,
is_training,
activation_fn='relu',
data_format='channels_last'):
"""Performs 1x1 conv followed by 2d or depthwise conv.
Args:
inputs: `Tensor` of size `[batch*time, height, width, channels]`. Only
supports 'channels_last' as the data format.
conv_type: 'string' of "std", "depth", "maxpool", or "avgpool" this selects
the spatial conv/pooling method.
kernel: `int` kernel size to be used for `conv2d` or max_pool2d` operations.
Should be a positive integer.
filters: `int` number of filters in the convolution.
stride: 'int' temporal stride
is_training: 'bool' specifying whether in training mode or not.
activation_fn: 'string' the activation function to use (relu or swish)
data_format: `str`. Only supports 'channels_last' as the data format.
Returns:
A `Tensor` of the same data_format
"""
if kernel == 1:
return inputs
use_relu = (activation_fn == 'relu')
if conv_type == 'std' or conv_type == 'depth':
inputs = conv2d(inputs, 1, filters, 1, is_training, use_relu=use_relu)
if not use_relu:
inputs = hard_swish(inputs)
if conv_type == 'std' or conv_type == '1std':
inputs = conv2d(inputs, int(kernel), filters, int(stride), is_training,
use_relu=use_relu)
if not use_relu:
inputs = hard_swish(inputs)
elif conv_type == 'depth':
depth_multiplier = 1
depthwise_kernel_shape = (int(kernel), int(kernel), inputs.shape[-1],
depth_multiplier)
depthwise_kernel = contrib_framework.model_variable(
name='depthwise_kernel',
shape=depthwise_kernel_shape,
dtype=tf.float32,
initializer=contrib_layers.variance_scaling_initializer(
factor=2.0, mode='FAN_IN', uniform=False),
trainable=True)
inputs = tf.nn.depthwise_conv2d(
inputs,
tf.cast(depthwise_kernel, inputs.dtype),
strides=[1, int(stride), int(stride), 1],
padding='SAME',
rate=[1, 1],
data_format='NHWC' if data_format == 'channels_last' else 'NCHW')
inputs = bn.batch_norm_relu(
inputs,
is_training,
relu=use_relu,
data_format=data_format)
if not use_relu:
inputs = hard_swish(inputs)
elif conv_type == 'maxpool':
inputs = tf.layers.max_pooling2d(
inputs,
int(kernel),
int(stride),
padding='same',
data_format=data_format)
elif conv_type == 'avgpool':
inputs = tf.layers.average_pooling2d(
inputs,
int(kernel),
int(stride),
padding='same',
data_format=data_format)
return inputs
def matrix_pool_sparse(
inputs, #pool the tensor: input: N x M x K along two dimensions
layer_params,
verbose=1,
scope=None,
**kwargs):
inp_values = inputs['input']
units_in = inputs['units']
mask_indices = inputs['mask_indices']
pool_mode = layer_params.get('pool_mode', 'max') #max or average pooling
mode = layer_params.get('mode', 'dense')
shape = inputs['shape']
N, M = shape
eps = tf.convert_to_tensor(1e-3, dtype=np.float32)
with tf.variable_scope(scope, default_name="matrix_sparse"):
theta_n = model_variable("theta_n",
shape=[units_in, units_in],
trainable=True,
dtype=tf.float32)
theta_m = model_variable("theta_m",
shape=[units_in, units_in],
trainable=True,
dtype=tf.float32)
if pool_mode is 'mean':
norm_0 = sparse_marginalize_mask(
mask_indices, shape=shape, axis=0, keep_dims=True) + eps
norm_1 = sparse_marginalize_mask(
mask_indices, shape=shape, axis=1, keep_dims=True) + eps
nvec = sparse_reduce(mask_indices,
inp_values,
units_in,
mode='sum',
shape=shape,
axis=1,
keep_dims=True) / norm_1
mvec = sparse_reduce(mask_indices,
inp_values,
units_in,
mode='sum',
shape=shape,
axis=0,
keep_dims=True) / norm_0
else:
nvec = sparse_reduce(mask_indices,
inp_values,
units_in,
mode=pool_mode,
shape=shape,
axis=1,
keep_dims=True)
mvec = sparse_reduce(mask_indices,
inp_values,
units_in,
mode=pool_mode,
shape=shape,
axis=0,
keep_dims=True)
nvec = tf.tensordot(nvec, theta_n, axes=1)
nvec.set_shape([N, 1, units_in]) #because of current tensorflow bug!!
mvec = tf.tensordot(mvec, theta_m, axes=1)
mvec.set_shape([1, M, units_in]) #because of current tensorflow bug!!
outdic = {
'nvec': nvec,
'mvec': mvec,
'mask_indices': mask_indices,
'units': units_in,
'shape': shape
}
return outdic
def matrix_sparse(inputs,
layer_params,
reuse=None,
scope=None,
verbose=1,
**kwargs):
units = layer_params.get('units')
if not scope:
scope = "matrix_sparse"
with tf.variable_scope(
scope,
default_name="matrix_sparse",
initializer=layer_params.get('kernel_initializer', None),
regularizer=layer_params.get('regularizer', None),
reuse=reuse,
):
#we should have the input matrix or at least one vector per dimension
assert (('nvec' in inputs and 'mvec' in inputs) or 'input' in inputs)
mat_values = inputs.get('input', None) #N x M x K
mask_indices = inputs.get('mask_indices', None)
skip_connections = layer_params.get('skip_connections', False)
shape = inputs['shape']
N, M = shape
K = inputs['units']
output = tf.convert_to_tensor(0, np.float32)
if mat_values is not None: #if we have an input matrix. If not, we only have nvec and mvec, i.e., user and movie properties
if 'max' in layer_params.get('pool_mode', 'max'):
mat_marg_0 = sparse_reduce(mask_indices,
mat_values,
K,
shape=shape,
mode='max',
axis=0,
keep_dims=True)
mat_marg_1 = sparse_reduce(mask_indices,
mat_values,
K,
shape=shape,
mode='max',
axis=1,
keep_dims=True)
mat_marg_2 = sparse_reduce(mask_indices,
mat_values,
K,
shape=shape,
mode='max',
axis=None,
keep_dims=True)
elif layer_params['pool_mode'] == 'mean':
mat_marg_0 = weighted_mean_reduce(mask_indices,
mat_values,
K,
shape=shape,
logweights=inputs.get(
'weights_row', None),
axis=0) # 1 x M x K
mat_marg_1 = weighted_mean_reduce(mask_indices,
mat_values,
K,
shape=shape,
logweights=inputs.get(
'weights_col', None),
axis=1) # N x 1 x K
mat_marg_2 = weighted_mean_reduce(mask_indices,
mat_values,
K,
shape=shape,
logweights=inputs.get(
'weights_both', None),
axis=None) # 1 x 1 x K
else:
raise KeyError("Unrecognised pool mode: %s" %
layer_params["pool_mode"])
theta_0 = model_variable("theta_0",
shape=[K, units],
trainable=True,
dtype=tf.float32)
theta_1 = model_variable("theta_1",
shape=[K, units],
trainable=True,
dtype=tf.float32)
theta_2 = model_variable("theta_2",
shape=[K, units],
trainable=True,
dtype=tf.float32)
theta_3 = model_variable("theta_3",
shape=[K, units],
trainable=True,
dtype=tf.float32)
output = sparse_tensordot_sparse(mat_values, theta_0, K)
output_0 = tf.tensordot(mat_marg_0, theta_1,
axes=[[2], [0]]) # 1 x M x units
output = sparse_tensor_broadcast_dense_add(output,
output_0,
mask_indices,
units,
broadcast_axis=0)
output_1 = tf.tensordot(mat_marg_1, theta_2,
axes=[[2], [0]]) # N x 1 x units
output = sparse_tensor_broadcast_dense_add(output,
output_1,
mask_indices,
units,
broadcast_axis=1)
output_2 = tf.tensordot(mat_marg_2, theta_3,
axes=[[2], [0]]) # 1 x 1 x units
output = sparse_tensor_broadcast_dense_add(output,
output_2,
mask_indices,
units,
broadcast_axis=None)
nvec = inputs.get('nvec', None)
mvec = inputs.get('mvec', None)
if nvec is not None:
theta_4 = model_variable("theta_4",
shape=[K, units],
trainable=True)
output_tmp = tf.tensordot(nvec, theta_4,
axes=[[2], [0]]) # N x 1 x units
output_tmp.set_shape([N, 1,
units]) #because of current tensorflow bug!!
if mat_values is not None:
output = sparse_tensor_broadcast_dense_add(output,
output_tmp,
mask_indices,
units,
broadcast_axis=1)
else:
# output = output + output_tmp
output = dense_vector_to_sparse_values(output_tmp,
mask_indices) + output
if mvec is not None:
theta_5 = model_variable("theta_5",
shape=[K, units],
trainable=True)
output_tmp = tf.tensordot(mvec, theta_5,
axes=[[2], [0]]) # 1 x M x units
output_tmp.set_shape([1, M,
units]) #because of current tensorflow bug!!
if mat_values is not None:
output = sparse_tensor_broadcast_dense_add(output,
output_tmp,
mask_indices,
units,
broadcast_axis=0)
else:
# output = output + output_tmp
output = dense_vector_to_sparse_values(output_tmp,
mask_indices) + output
if layer_params.get("individual_bias", False):
# for testing my individual bias idea - I don't think it is helpful
print(
"Using individual bias in scope %s" %
tf.contrib.framework.get_name_scope(), kwargs["sizes"])
row_bias = model_variable("row_bias",
shape=[kwargs["sizes"][0]],
trainable=True)
column_bias = model_variable("column_bias",
shape=[kwargs["sizes"][1]],
trainable=True)
#mask_indices = tf.cast(mask_indices, dtype=tf.float32)
r_bias = tf.reshape(tf.gather(column_bias, inputs['col']),
(1, -1, 1))
c_bias = tf.reshape(tf.gather(row_bias, inputs['row']), (-1, 1, 1))
output += r_bias - tf.reduce_mean(r_bias)
output += c_bias - tf.reduce_mean(c_bias)
if layer_params.get('activation', None) is not None:
if verbose == 1:
print("Applying activation: %s" % layer_params["activation"])
output = layer_params.get('activation')(output)
if skip_connections and mat_values is not None and K == units:
output = output + mat_values
# if mat_values is None:
# output = dense_tensor_to_sparse_values(output, mask_indices, units)
outdic = {
'input': output,
'mask_indices': mask_indices,
'units': units,
'shape': shape
}
if layer_params.get("attention_pooling", False):
gamma_0 = model_variable("gamma_0",
shape=[K, units],
trainable=True,
dtype=tf.float32)
gamma_1 = model_variable("gamma_1",
shape=[K, units],
trainable=True,
dtype=tf.float32)
gamma_2 = model_variable("gamma_2",
shape=[K, units],
trainable=True,
dtype=tf.float32)
c = 1.
if mat_values is not None:
outdic["weights_row"] = tf.tensordot(mat_marg_1 * c,
gamma_2,
axes=[[2], [0]])
outdic["weights_col"] = tf.tensordot(mat_marg_0 * c,
gamma_1,
axes=[[2], [0]])
outdic["weights_both"] = sparse_tensordot_sparse(
mat_values * c, gamma_0, K)
else:
outdic["weights_row"] = tf.tensordot(nvec * c,
gamma_2,
axes=[[2], [0]])
outdic["weights_col"] = tf.tensordot(mvec * c,
gamma_1,
axes=[[2], [0]])
return outdic
def matrix_dense(inputs,
layer_params,
reuse=None,
scope=None,
verbose=1,
**kwargs):
'''
mat, # N x M x K input matrix
mask = None, # N x M x 1 the observed entries
nvec = None, # N x 1 x K' features for rows
mvec = None, # 1 x M x K'' features for cols
'''
units = layer_params.get('units')
if not scope:
scope = "matrix_dense"
with tf.variable_scope(
scope,
default_name="matrix_dense",
initializer=layer_params.get('kernel_initializer', None),
regularizer=layer_params.get('regularizer', None),
reuse=reuse,
):
#we should have the input matrix or at least one vector per dimension
assert (('nvec' in inputs and 'mvec' in inputs) or 'input' in inputs)
eps = tf.convert_to_tensor(1e-3, dtype=np.float32)
mat = inputs.get('input', None) #N x M x K
mask = inputs.get('mask', None) #N x M
skip_connections = layer_params.get('skip_connections', False)
output = tf.convert_to_tensor(0, np.float32)
config_string = "Using the following terms: "
sign = 1
overparam = layer_params.get('overparam', False)
indn = inputs['indn']
indm = inputs['indm']
if layer_params.get('bias', True):
bias = model_variable("bias", shape=[units], trainable=True)
output += sign * bias
sign *= -1
if mat is not None: #if we have an input matrix. If not, we only have nvec and mvec, i.e., user and movie properties
N, M, K = mat.get_shape().as_list()
norm_N = np.float32(N)
norm_M = np.float32(M)
norm_NM = np.float32(N * M)
if mask is not None:
mat = mat * mask
norm_N = tf.reduce_sum(mask, axis=0,
keep_dims=True) + eps # 1, M, 1
norm_M = tf.reduce_sum(mask, axis=1,
keep_dims=True) + eps # N, 1, 1
norm_NM = tf.reduce_sum(mask, axis=[0, 1],
keep_dims=True) + eps # 1, 1, 1
if 'max' in layer_params.get('pool_mode', 'max') and mask is None:
mat_marg_0 = tf.reduce_max(mat, axis=0, keep_dims=True)
mat_marg_1 = tf.reduce_max(mat, axis=1, keep_dims=True)
mat_marg_2 = tf.reduce_max(mat_marg_0, axis=1, keep_dims=True)
else:
mat_marg_0 = tf.reduce_sum(
mat, axis=0, keep_dims=True) / norm_N # 1 x M x K
mat_marg_1 = tf.reduce_sum(
mat, axis=1, keep_dims=True) / norm_M # N x 1 x K
mat_marg_2 = tf.reduce_sum(
mat_marg_0, axis=1, keep_dims=True) / norm_NM # 1 x 1 x K
if layer_params.get('theta_0', True):
config_string += "theta 0, "
theta_0 = model_variable("theta_0",
shape=[K, units],
trainable=True)
output += sign * tf.tensordot(
mat,
theta_0,
axes=tf.convert_to_tensor([[2], [0]],
dtype=np.int32)) # N x M x units
output.set_shape([N, M,
units]) #because of current tensorflow bug!!
sign *= -1
if layer_params.get('theta_1', True):
config_string += "theta 1, "
if overparam:
MM = inputs['total_shape'][1]
theta_1 = tf.get_variable('theta_1',
shape=[MM, K, units],
trainable=True)
theta_1 = tf.gather(theta_1, indm, axis=0)
theta_1.set_shape([M, K, units])
output += sign * tf.einsum('ijk,jkl->ijl', mat_marg_0,
theta_1) # 1 x M x units
else:
theta_1 = model_variable("theta_1",
shape=[K, units],
trainable=True)
output += sign * tf.tensordot(
mat_marg_0,
theta_1,
axes=tf.convert_to_tensor(
[[2], [0]], dtype=np.int32)) # 1 x M x units
output.set_shape([N, M,
units]) #because of current tensorflow bug!!
sign *= -1
if layer_params.get('theta_2', True):
config_string += "theta 2, "
if overparam:
NN = inputs['total_shape'][0]
theta_2 = tf.get_variable('theta_2',
shape=[NN, K, units],
trainable=True)
theta_2 = tf.gather(theta_2, indn, axis=0)
theta_2.set_shape([N, K, units])
output += sign * tf.einsum('ijk,ikl->ijl', mat_marg_1,
theta_2)
else:
theta_2 = model_variable("theta_2",
shape=[K, units],
trainable=True)
output += sign * tf.tensordot(
mat_marg_1,
theta_2,
axes=tf.convert_to_tensor(
[[2], [0]], dtype=np.int32)) # N x 1 x units
output.set_shape([N, M,
units]) #because of current tensorflow bug!!
sign *= -1
if layer_params.get('theta_3', True):
config_string += "theta 3, "
theta_3 = model_variable("theta_3",
shape=[K, units],
trainable=True)
output += sign * tf.tensordot(
mat_marg_2,
theta_3,
axes=tf.convert_to_tensor([[2], [0]],
dtype=np.int32)) # 1 x 1 x units
output.set_shape([N, M,
units]) #because of current tensorflow bug!!
sign *= -1
nvec = inputs.get('nvec', None)
mvec = inputs.get('mvec', None)
if layer_params.get('bilinear', False):
if nvec is not None:
config_string += "bilinear, "
_, _, K = nvec.get_shape().as_list()
theta_6 = model_variable("theta_6",
shape=[K, K, units],
trainable=True)
output_n = tf.reduce_sum(nvec[:, :, :, None, None] *
theta_6[None, None, :, :, :],
axis=2)
output_m = tf.reduce_sum(output_n * mvec[:, :, :, None],
axis=2)
output += sign * output_m
sign *= -1
if layer_params.get('theta_4', True):
if nvec is not None:
config_string += "theta 4, "
N, _, K = nvec.get_shape().as_list()
theta_4 = model_variable("theta_4",
shape=[K, units],
trainable=True)
output_tmp = tf.tensordot(
nvec,
theta_4,
axes=tf.convert_to_tensor([[2], [0]],
dtype=np.int32)) # N x 1 x units
output_tmp.set_shape([N, 1, units
]) #because of current tensorflow bug!!
output += sign * output_tmp
sign *= -1
if layer_params.get('theta_5', True):
if mvec is not None:
config_string += "theta 5, "
_, M, K = mvec.get_shape().as_list()
theta_5 = model_variable("theta_5",
shape=[K, units],
trainable=True)
output_tmp = tf.tensordot(
mvec,
theta_5,
axes=tf.convert_to_tensor([[2], [0]],
dtype=np.int32)) # 1 x M x units
output_tmp.set_shape([1, M, units
]) #because of current tensorflow bug!!
output += sign * output_tmp
sign *= -1
if layer_params.get('activation', None) is not None:
output = layer_params.get('activation')(output)
if layer_params.get('drop_mask', True):
mask = None
if skip_connections and mat is not None and K == units:
config_string += "with skip connections"
output = output + mat
print(config_string)
outdic = {
'input': output,
'mask': mask,
'total_shape': inputs['total_shape'],
'indn': indn,
'indm': indm
}
return outdic
def main(opts):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
path = opts['data_path']
data = get_data(path, train=.8, valid=.1, test=.1)
#build encoder and decoder and use VAE loss
N, M, num_features = data['mat_shape']
maxN, maxM = opts['maxN'], opts['maxM']
if N < maxN: maxN = N
if M < maxM: maxM = M
if opts['verbose'] > 0:
print('\nFactorized Autoencoder run settings:')
print('dataset: ', path)
print('Exchangable layer pool mode: ',
opts['defaults']['matrix_sparse']['pool_mode'])
print('Pooling layer pool mode: ',
opts['defaults']['matrix_pool_sparse']['pool_mode'])
print('learning rate: ', opts['lr'])
print('activation: ', opts['defaults']['matrix_sparse']['activation'])
print('number of latent features: ', opts['encoder'][-2]['units'])
print('maxN: ', opts['maxN'])
print('maxM: ', opts['maxM'])
print('')
with tf.Graph().as_default():
# with tf.device('/gpu:0'):
mat_values_tr = tf.placeholder(tf.float32,
shape=[None],
name='mat_values_tr')
mask_indices_tr = tf.placeholder(tf.int32,
shape=[None, 2],
name='mask_indices_tr')
mat_values_val = tf.placeholder(tf.float32,
shape=[None],
name='mat_values_val')
mask_indices_val = tf.placeholder(tf.int32,
shape=[None, 2],
name='mask_indices_val')
mask_indices_tr_val = tf.placeholder(tf.int32,
shape=[None, 2],
name='mask_indices_tr_val')
with tf.variable_scope(
None,
default_name="input_features",
initializer=opts['defaults']['matrix_sparse'].get(
'kernel_initializer', None),
regularizer=opts['defaults']['matrix_sparse'].get(
'regularizer', None),
reuse=False,
):
mvec_feat = model_variable("mvec_feat",
shape=[1, M, 1],
trainable=True)
nvec_feat = model_variable("nvec_feat",
shape=[N, 1, 1],
trainable=True)
with tf.variable_scope("encoder"):
tr_dict = {
'input': mat_values_tr,
'mask_indices': mask_indices_tr,
'units': 1,
'mvec': mvec_feat,
'shape': [N, M],
'nvec': nvec_feat
}
#with tf.variable_scope("encoder"):
# tr_dict = {'input':mat_values_tr,
# 'mask_indices':mask_indices_tr,
# 'units':1}
val_dict = {
'input': mat_values_tr,
'mask_indices': mask_indices_tr,
'units': 1,
'mvec': mvec_feat,
'nvec': nvec_feat,
'shape': [N, M]
}
encoder = Model(layers=opts['encoder'],
layer_defaults=opts['defaults'],
verbose=2) #define the encoder
out_enc_tr = encoder.get_output(tr_dict) #build the encoder
out_enc_val = encoder.get_output(
val_dict, reuse=True, verbose=0,
is_training=False) #get encoder output, reusing the neural net
with tf.variable_scope("decoder"):
tr_dict = { #'nvec':out_enc_tr['nvec'],
#'mvec':out_enc_tr['mvec'],
'input':
masked_inner_product(out_enc_tr['nvec'], out_enc_tr['mvec'],
mask_indices_tr),
'mask_indices':
mask_indices_tr,
'units':
1, #out_enc_tr['units'],
'shape':
out_enc_tr['shape']
}
val_dict = { #'nvec':out_enc_val['nvec'],
#'mvec':out_enc_val['mvec'],
'input':
masked_inner_product(out_enc_val['nvec'], out_enc_val['mvec'],
mask_indices_tr_val),
'mask_indices':
mask_indices_tr_val,
'units':
1, #out_enc_val['units'],
'shape':
out_enc_val['shape']
}
decoder = Model(layers=opts['decoder'],
layer_defaults=opts['defaults'],
verbose=2) #define the decoder
out_dec_tr = decoder.get_output(tr_dict) #build it
out_tr = out_dec_tr['input']
out_dec_val = decoder.get_output(
val_dict, reuse=True, verbose=0,
is_training=False) #reuse it for validation
out_val = out_dec_val['input']
#loss and training
rec_loss = rec_loss_fn_sp(mat_values_tr, mask_indices_tr, out_tr,
tf.ones(tf.shape(mat_values_tr)))
reg_loss = sum(tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)) # regularization
rec_loss_val = rec_loss_fn_sp(mat_values_val, mask_indices_val,
out_val, data['mask_tr_val_split'])
total_loss = rec_loss + reg_loss
train_step = tf.train.AdamOptimizer(opts['lr']).minimize(total_loss)
#train_step = tf.train.GradientDescentOptimizer(opts['lr']).minimize(total_loss)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.run(tf.global_variables_initializer())
if 'by_row_column_density' in opts['sample_mode']:
iters_per_epoch = math.ceil(N // maxN) * math.ceil(
M // maxM
) # a bad heuristic: the whole matrix is in expectation covered in each epoch
elif 'uniform_over_dense_values' in opts['sample_mode']:
minibatch_size = np.minimum(opts['minibatch_size'],
data['mask_indices_tr'].shape[0])
iters_per_epoch = data['mask_indices_tr'].shape[0] // minibatch_size
min_loss = 5
min_loss_epoch = 0
losses = OrderedDict()
losses["train"] = []
losses["valid"] = []
for ep in range(opts['epochs']):
begin = time.time()
loss_tr_, rec_loss_tr_, loss_val_, loss_ts_ = 0, 0, 0, 0
if 'by_row_column_density' in opts['sample_mode']:
for indn_, indm_ in tqdm(
sample_submatrix(data['mask_tr'],
maxN,
maxM,
sample_uniform=False),
total=iters_per_epoch): #go over mini-batches
inds_ = np.ix_(
indn_, indm_, [0]
) #select a sub-matrix given random indices for users/movies
mat_sp = data['mat_tr_val'][inds_] * data['mask_tr'][inds_]
mat_values = dense_array_to_sparse(mat_sp)['values']
mask_indices = dense_array_to_sparse(
data['mask_tr'][inds_])['indices'][:, 0:2]
tr_dict = {
mat_values_tr: mat_values,
mask_indices_tr: mask_indices
}
_, bloss_, brec_loss_ = sess.run(
[train_step, total_loss, rec_loss], feed_dict=tr_dict)
loss_tr_ += np.sqrt(bloss_)
rec_loss_tr_ += np.sqrt(brec_loss_)
elif 'uniform_over_dense_values' in opts['sample_mode']:
for sample_ in tqdm(sample_dense_values_uniform(
data['mask_indices_tr'], minibatch_size,
iters_per_epoch),
total=iters_per_epoch):
mat_values = data['mat_values_tr'][sample_]
mask_indices = data['mask_indices_tr'][sample_]
tr_dict = {
mat_values_tr: mat_values,
mask_indices_tr: mask_indices
}
_, bloss_, brec_loss_ = sess.run(
[train_step, total_loss, rec_loss], feed_dict=tr_dict)
loss_tr_ += np.sqrt(bloss_)
rec_loss_tr_ += np.sqrt(brec_loss_)
else:
print('\nERROR - unknown <sample_mode> in main()\n')
return
loss_tr_ /= iters_per_epoch
rec_loss_tr_ /= iters_per_epoch
new_nvec, new_mvec = sess.run([nvec_feat, mvec_feat])
## Validation Loss
val_dict = {
mat_values_tr: data['mat_values_tr'],
mask_indices_tr: data['mask_indices_tr'],
mat_values_val: data['mat_values_tr_val'],
mask_indices_val: data['mask_indices_val'],
mask_indices_tr_val: data['mask_indices_tr_val']
}
bloss_, = sess.run([rec_loss_val], feed_dict=val_dict)
loss_val_ += np.sqrt(bloss_)
if loss_val_ < min_loss: # keep track of the best validation loss
min_loss = loss_val_
min_loss_epoch = ep
losses['train'].append(loss_tr_)
losses['valid'].append(loss_val_)
print(
"epoch {:d} took {:.1f} training loss {:.3f} (rec:{:.3f}) \t validation: {:.3f} \t minimum validation loss: {:.3f} at epoch: {:d} \t test loss: {:.3f}"
.format(ep,
time.time() - begin, loss_tr_, rec_loss_tr_, loss_val_,
min_loss, min_loss_epoch, loss_ts_))
return losses
def normalize_to_target(inputs,
target_norm_value,
dim,
epsilon=1e-7,
trainable=True,
scope='NormalizeToTarget',
summarize=True):
"""L2 normalizes the inputs across the specified dimension to a target norm.
This op implements the L2 Normalization layer introduced in
Liu, Wei, et al. "SSD: Single Shot MultiBox Detector."
and Liu, Wei, Andrew Rabinovich, and Alexander C. Berg.
"Parsenet: Looking wider to see better." and is useful for bringing
activations from multiple layers in a convnet to a standard scale.
Note that the rank of `inputs` must be known and the dimension to which
normalization is to be applied should be statically defined.
TODO(jonathanhuang): Add option to scale by L2 norm of the entire input.
Args:
inputs: A `Tensor` of arbitrary size.
target_norm_value: A float value that specifies an initial target norm or
a list of floats (whose length must be equal to the depth along the
dimension to be normalized) specifying a per-dimension multiplier
after normalization.
dim: The dimension along which the input is normalized.
epsilon: A small value to add to the inputs to avoid dividing by zero.
trainable: Whether the norm is trainable or not
scope: Optional scope for variable_scope.
summarize: Whether or not to add a tensorflow summary for the op.
Returns:
The input tensor normalized to the specified target norm.
Raises:
ValueError: If dim is smaller than the number of dimensions in 'inputs'.
ValueError: If target_norm_value is not a float or a list of floats with
length equal to the depth along the dimension to be normalized.
"""
with tf.variable_scope(scope, 'NormalizeToTarget', [inputs]):
if not inputs.get_shape():
raise ValueError('The input rank must be known.')
input_shape = inputs.get_shape().as_list()
input_rank = len(input_shape)
if dim < 0 or dim >= input_rank:
raise ValueError(
'dim must be non-negative but smaller than the input rank.')
if not input_shape[dim]:
raise ValueError('input shape should be statically defined along '
'the specified dimension.')
depth = input_shape[dim]
if not (isinstance(target_norm_value, float) or
(isinstance(target_norm_value, list)
and len(target_norm_value) == depth)
and all([isinstance(val, float)
for val in target_norm_value])):
raise ValueError(
'target_norm_value must be a float or a list of floats '
'with length equal to the depth along the dimension to '
'be normalized.')
if isinstance(target_norm_value, float):
initial_norm = depth * [target_norm_value]
else:
initial_norm = target_norm_value
target_norm = contrib_framework.model_variable(name='weights',
dtype=tf.float32,
initializer=tf.constant(
initial_norm,
dtype=tf.float32),
trainable=trainable)
if summarize:
mean = tf.reduce_mean(target_norm)
tf.summary.scalar(tf.get_variable_scope().name, mean)
lengths = epsilon + tf.sqrt(tf.reduce_sum(tf.square(inputs), dim,
True))
mult_shape = input_rank * [1]
mult_shape[dim] = depth
return tf.reshape(target_norm, mult_shape) * tf.truediv(
inputs, lengths)
