def __init__(self, network, ):
self.network = network
if isinstance(self.network, tfnn.ClfNetwork):
with tfnn.name_scope('accuracy'):
with tfnn.name_scope('correct_prediction'):
correct_prediction = tfnn.equal(tfnn.argmax(network.predictions, 1),
tfnn.argmax(network.target_placeholder, 1), name='correct_prediction')
with tfnn.name_scope('accuracy'):
self.accuracy = tfnn.reduce_mean(tfnn.cast(correct_prediction, tfnn.float32), name='accuracy')
tfnn.scalar_summary('accuracy', self.accuracy)
elif isinstance(self.network, tfnn.RegNetwork):
self.first_time_lm = True
self.first_time_soc = True
with tfnn.name_scope('r2_score'):
with tfnn.name_scope('ys_mean'):
ys_mean = tfnn.reduce_mean(network.target_placeholder, reduction_indices=[0], name='ys_mean')
with tfnn.name_scope('total_sum_squares'):
ss_tot = tfnn.reduce_sum(tfnn.square(network.target_placeholder - ys_mean),
reduction_indices=[0], name='total_sum_squares')
# ss_reg = np.sum(np.square(predictions-ys_mean), axis=0)
with tfnn.name_scope('residual_sum_squares'):
ss_res = tfnn.reduce_sum(tfnn.square(network.target_placeholder - network.predictions),
reduction_indices=[0], name='residual_sum_squares')
with tfnn.name_scope('coefficient_of_determination'):
self.r2_score = tfnn.sub(tfnn.constant(1, dtype=tfnn.float32), (ss_res / ss_tot)[0],
name='coefficient_of_determination')
tfnn.scalar_summary('r2_score', self.r2_score)
def _init_loss(self):
with tfnn.name_scope('predictions'):
if self.method == 'softmax':
self.predictions = tfnn.nn.softmax(self.layers_results['final'][-1], name='predictions')
elif self.method == 'sigmoid':
self.predictions = tfnn.nn.sigmoid(self.layers_results['final'][-1], name='predictions')
with tfnn.name_scope('loss'):
if self.method == 'softmax':
self.cross_entropy = tfnn.nn.softmax_cross_entropy_with_logits(
self.layers_results['final'][-1],
self.target_placeholder,
name='xentropy')
elif self.method == 'sigmoid':
self.cross_entropy = tfnn.nn.sigmoid_cross_entropy_with_logits(
self.layers_results['final'][-1],
self.target_placeholder,
name='xentropy')
else:
raise ValueError("method should be one of ['sparse_softmax', 'softmax', 'sigmoid']")
self.loss = tfnn.reduce_mean(self.cross_entropy, name='xentropy_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for layer in self.layers_results['Layer'][1:]:
regularizers += tfnn.nn.l2_loss(layer.W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)
def _set_r2(self):
if isinstance(self.network, tfnn.RegNetwork):
with tfnn.name_scope('r2_score'):
self.ys_mean = ys_mean = tfnn.reduce_mean(self.network.target_placeholder,
reduction_indices=[0],
name='ys_mean')
self.ss_tot = ss_tot = tfnn.reduce_sum(
tfnn.square(self.network.target_placeholder - ys_mean),
reduction_indices=[0], name='total_sum_squares')
# ss_reg = np.sum(np.square(predictions-ys_mean), axis=0)
self.ss_res = ss_res = tfnn.reduce_sum(
tfnn.square(self.network.target_placeholder - self.network.predictions),
reduction_indices=[0], name='residual_sum_squares')
self.aaa = ss_res / ss_tot
self.r2 = tfnn.reduce_mean(
tfnn.sub(tfnn.ones_like(ss_res, dtype=tfnn.float32), (ss_res / ss_tot)),
name='coefficient_of_determination')
tfnn.scalar_summary('r2_score', self.r2)
def _set_accuracy(self):
if isinstance(self.network, tfnn.ClfNetwork):
with tfnn.name_scope('accuracy'):
correct_prediction = tfnn.equal(
tfnn.argmax(self.network.predictions, 1),
tfnn.argmax(self.network.target_placeholder, 1),
name='correct_prediction')
self.accuracy = tfnn.reduce_mean(
tfnn.cast(correct_prediction, tfnn.float32), name='accuracy')
tfnn.scalar_summary('accuracy', self.accuracy)
def __init__(
self,
network,
):
self.network = network
if isinstance(self.network, tfnn.ClfNetwork):
with tfnn.name_scope('accuracy'):
with tfnn.name_scope('correct_prediction'):
correct_prediction = tfnn.equal(
tfnn.argmax(network.predictions, 1),
tfnn.argmax(network.target_placeholder, 1),
name='correct_prediction')
with tfnn.name_scope('accuracy'):
self.accuracy = tfnn.reduce_mean(tfnn.cast(
correct_prediction, tfnn.float32),
name='accuracy')
tfnn.scalar_summary('accuracy', self.accuracy)
elif isinstance(self.network, tfnn.RegNetwork):
self.first_time_lm = True
self.first_time_soc = True
with tfnn.name_scope('r2_score'):
with tfnn.name_scope('ys_mean'):
ys_mean = tfnn.reduce_mean(network.target_placeholder,
reduction_indices=[0],
name='ys_mean')
with tfnn.name_scope('total_sum_squares'):
ss_tot = tfnn.reduce_sum(
tfnn.square(network.target_placeholder - ys_mean),
reduction_indices=[0],
name='total_sum_squares')
# ss_reg = np.sum(np.square(predictions-ys_mean), axis=0)
with tfnn.name_scope('residual_sum_squares'):
ss_res = tfnn.reduce_sum(
tfnn.square(network.target_placeholder -
network.predictions),
reduction_indices=[0],
name='residual_sum_squares')
with tfnn.name_scope('coefficient_of_determination'):
self.r2_score = tfnn.sub(tfnn.constant(1, dtype=tfnn.float32),
(ss_res / ss_tot)[0],
name='coefficient_of_determination')
tfnn.scalar_summary('r2_score', self.r2_score)
def _set_accuracy(self):
if isinstance(self.network, tfnn.ClfNetwork):
with tfnn.name_scope('accuracy'):
correct_prediction = tfnn.equal(
tfnn.argmax(self.network.predictions, 1),
tfnn.argmax(self.network.target_placeholder, 1),
name='correct_prediction')
self.accuracy = tfnn.reduce_mean(tfnn.cast(
correct_prediction, tfnn.float32),
name='accuracy')
tfnn.scalar_summary('accuracy', self.accuracy)
def _set_r2(self):
if isinstance(self.network, tfnn.RegNetwork):
with tfnn.name_scope('r2_score'):
self.ys_mean = ys_mean = tfnn.reduce_mean(
self.network.target_placeholder,
reduction_indices=[0],
name='ys_mean')
self.ss_tot = ss_tot = tfnn.reduce_sum(
tfnn.square(self.network.target_placeholder - ys_mean),
reduction_indices=[0],
name='total_sum_squares')
# ss_reg = np.sum(np.square(predictions-ys_mean), axis=0)
self.ss_res = ss_res = tfnn.reduce_sum(
tfnn.square(self.network.target_placeholder -
self.network.predictions),
reduction_indices=[0],
name='residual_sum_squares')
self.aaa = ss_res / ss_tot
self.r2 = tfnn.reduce_mean(tfnn.sub(
tfnn.ones_like(ss_res, dtype=tfnn.float32),
(ss_res / ss_tot)),
name='coefficient_of_determination')
tfnn.scalar_summary('r2_score', self.r2)
def _init_loss(self):
with tfnn.name_scope('predictions'):
self.predictions = self.layers_final_output.iloc[-1] + 0
with tfnn.name_scope('loss'):
loss_square = tfnn.square(self.target_placeholder - self.layers_final_output.iloc[-1], name='loss_square')
loss_sum = tfnn.reduce_sum(loss_square, reduction_indices=[1], name='loss_sum')
self.loss = tfnn.reduce_mean(loss_sum, name='loss_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for W in self.Ws:
regularizers += tfnn.nn.l2_loss(W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)
def _init_loss(self):
with tfnn.name_scope('predictions'):
self.predictions = self.layers_results['final'][-1]
with tfnn.name_scope('loss'):
loss_square = tfnn.square(self.target_placeholder - self.predictions,
name='loss_square')
loss_sum = tfnn.reduce_sum(loss_square, reduction_indices=[1], name='loss_sum')
self.loss = tfnn.reduce_mean(loss_sum, name='loss_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for layer in self.layers_results['Layer'][1:]:
regularizers += tfnn.nn.l2_loss(layer.W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)
def _init_loss(self):
with tfnn.name_scope('predictions'):
self.predictions = self.layers_final_output.iloc[-1] + 0
with tfnn.name_scope('loss'):
loss_square = tfnn.square(self.target_placeholder -
self.layers_final_output.iloc[-1],
name='loss_square')
loss_sum = tfnn.reduce_sum(loss_square,
reduction_indices=[1],
name='loss_sum')
self.loss = tfnn.reduce_mean(loss_sum, name='loss_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for W in self.Ws:
regularizers += tfnn.nn.l2_loss(W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)
def _init_loss(self):
with tfnn.name_scope('predictions'):
self.predictions = self.layers_results['final'][-1]
with tfnn.name_scope('loss'):
loss_square = tfnn.square(self.target_placeholder -
self.predictions,
name='loss_square')
loss_sum = tfnn.reduce_sum(loss_square,
reduction_indices=[1],
name='loss_sum')
self.loss = tfnn.reduce_mean(loss_sum, name='loss_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for layer in self.layers_results['Layer'][1:]:
regularizers += tfnn.nn.l2_loss(layer.W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)
def _init_loss(self):
with tfnn.name_scope('predictions'):
if self.method == 'softmax':
self.predictions = tfnn.nn.softmax(
self.layers_final_output.iloc[-1], name='predictions')
elif self.method == 'sigmoid':
self.predictions = tfnn.nn.sigmoid(
self.layers_final_output.iloc[-1], name='predictions')
with tfnn.name_scope('loss'):
if self.method == 'softmax':
self.cross_entropy = tfnn.nn.softmax_cross_entropy_with_logits(
self.layers_final_output.iloc[-1],
self.target_placeholder,
name='xentropy')
elif self.method == 'sigmoid':
self.cross_entropy = tfnn.nn.sigmoid_cross_entropy_with_logits(
self.layers_final_output.iloc[-1],
self.target_placeholder,
name='xentropy')
else:
raise ValueError(
"method should be one of ['sparse_softmax', 'softmax', 'sigmoid']"
)
self.loss = tfnn.reduce_mean(self.cross_entropy,
name='xentropy_mean')
if self.reg == 'l2':
with tfnn.name_scope('l2_reg'):
regularizers = 0
for W in self.Ws:
regularizers += tfnn.nn.l2_loss(W, name='l2_reg')
regularizers *= self.l2_placeholder
with tfnn.name_scope('l2_loss'):
self.loss += regularizers
tfnn.scalar_summary('loss', self.loss)