def __init__(self, input_shape, graph):
with graph.as_default():
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.inp = tf.placeholder(tf.float32, [None, input_shape], name="input")
self.out = tf.placeholder(tf.float32, [None], name="output")
self.weights = tf.get_variable("weights", [input_shape, 1])
self.bias = tf.get_variable("bias", [1])
self.predictions, self.loss = mean_squared_error_regressor(
self.inp, self.out, self.weights, self.bias)
def linear_regression(X, y):
"""Creates linear regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features.
y: tensor or placeholder for target.
Returns:
Predictions and loss tensors.
"""
with tf.variable_scope('linear_regression'):
weights = tf.get_variable('weights', [X.get_shape()[1], 1])
bias = tf.get_variable('bias', [1])
return mean_squared_error_regressor(X, y, weights, bias)
def linear_regression(X, y):
"""Creates linear regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features.
y: tensor or placeholder for target.
Returns:
Predictions and loss tensors.
"""
with tf.variable_scope('linear_regression'):
weights = tf.get_variable('weights', [X.get_shape()[1], 1])
bias = tf.get_variable('bias', [1])
return mean_squared_error_regressor(X, y, weights, bias)
def linear_regression(X, y):
"""Creates linear regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features.
y: tensor or placeholder for target.
Returns:
Predictions and loss tensors.
"""
with tf.variable_scope('linear_regression'):
tf.histogram_summary('linear_regression.X', X)
tf.histogram_summary('linear_regression.y', y)
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
weights = tf.get_variable('weights', [X.get_shape()[1], output_shape])
bias = tf.get_variable('bias', [output_shape])
tf.histogram_summary('linear_regression.weights', weights)
tf.histogram_summary('linear_regression.bias', bias)
return mean_squared_error_regressor(X, y, weights, bias)
def linear_regression(X, y):
"""Creates linear regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features.
y: tensor or placeholder for target.
Returns:
Predictions and loss tensors.
"""
with tf.variable_scope('linear_regression'):
tf.histogram_summary('linear_regression.X', X)
tf.histogram_summary('linear_regression.y', y)
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
weights = tf.get_variable('weights', [X.get_shape()[1], output_shape])
bias = tf.get_variable('bias', [output_shape])
tf.histogram_summary('linear_regression.weights', weights)
tf.histogram_summary('linear_regression.bias', bias)
return mean_squared_error_regressor(X, y, weights, bias)
