我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用tensorflow.python.ops.init_ops.random_normal_initializer()。
def conv_layer(self, bottom, in_channels, out_channels, name): with tf.variable_scope(name): filter_size_h, filter_size_w = self.__convKernelSize filt = tf.get_variable(name=name + "_filters", shape=[filter_size_h, filter_size_w, in_channels, out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) conv_biases = tf.get_variable(name=name + "_biases", shape=[out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) def _inner_conv(bott): conv = tf.nn.conv2d(bott, filt, [1, 1, 1, 1], padding='SAME') bias = tf.nn.bias_add(conv, conv_biases) relu = tf.nn.relu(bias) return relu _bottoms = tf.unstack(bottom, axis=0) output = tf.stack([_inner_conv(bott) for bott in _bottoms], axis=0) return output
def random_normal_variable(shape, mean, scale, dtype=None, name=None, seed=None): """Instantiates a variable with values drawn from a normal distribution. Arguments: shape: Tuple of integers, shape of returned Keras variable. mean: Float, mean of the normal distribution. scale: Float, standard deviation of the normal distribution. dtype: String, dtype of returned Keras variable. name: String, name of returned Keras variable. seed: Integer, random seed. Returns: A Keras variable, filled with drawn samples. Example: ```python # TensorFlow example >>> kvar = K.random_normal_variable((2,3), 0, 1) >>> kvar <tensorflow.python.ops.variables.Variable object at 0x10ab12dd0> >>> K.eval(kvar) array([[ 1.19591331, 0.68685907, -0.63814116], [ 0.92629528, 0.28055015, 1.70484698]], dtype=float32)
""" if dtype is None: dtype = floatx() shape = tuple(map(int, shape)) tf_dtype = _convert_string_dtype(dtype) if seed is None: # ensure that randomness is conditioned by the Numpy RNG seed = np.random.randint(10e8) value = init_ops.random_normal_initializer( mean, scale, dtype=tf_dtype, seed=seed)(shape) return variable(value, dtype=dtype, name=name)
```
def separable_conv(self, bottom, in_channels, out_channels, name): with tf.variable_scope(name): filter_size_h = 1 filter_size_w = 1 filt = tf.get_variable(name=name + "_filters", shape=[filter_size_h, filter_size_w, in_channels, out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) conv_biases = tf.get_variable(name=name + "_biases", shape=[out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME') bias = tf.nn.bias_add(conv, conv_biases) relu = tf.nn.relu(bias) return relu
def _conv(self, input, in_channels, out_channels, name): with tf.variable_scope(name): filter_size_h, filter_size_w = self.__convKernelSize filt = tf.get_variable(name=name + "_filters", shape=[filter_size_h, filter_size_w, in_channels, out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) conv_biases = tf.get_variable(name=name + "_biases", shape=[out_channels], initializer=init_ops.random_normal_initializer(stddev=0.01)) conv = tf.nn.conv2d(input, filt, [1, 1, 1, 1], padding='SAME') bias = tf.nn.bias_add(conv, conv_biases) relu = tf.nn.relu(bias) return relu
def fc_layer(self, bottom, out_size, name): with tf.variable_scope(name): _, _height, _width, _channel = bottom.get_shape().as_list() size = _height*_width*_channel weights = tf.get_variable(name=name + "_weights", shape = [size, out_size], initializer=init_ops.random_normal_initializer(stddev=0.01)) biases = tf.get_variable(name=name + "_biases", shape=[out_size], initializer=init_ops.random_normal_initializer(stddev=0.01)) print weights x = tf.reshape(bottom, [-1, size]) fc = tf.nn.bias_add(tf.matmul(x, weights), biases) return fc
def _fc(self, bottom, out_size, name): with tf.variable_scope(name): _, size = bottom.get_shape().as_list() weights = tf.get_variable(name=name + "_weights", shape = [size, out_size], initializer=init_ops.random_normal_initializer(stddev=0.01)) biases = tf.get_variable(name=name + "_biases", shape=[out_size], initializer=init_ops.random_normal_initializer(stddev=0.01)) print weights fc = tf.nn.bias_add(tf.matmul(bottom, weights), biases) return fc
def linear_regression(x, y, init_mean=None, init_stddev=1.0): """Creates linear regression TensorFlow subgraph. Args: x: tensor or placeholder for input features. y: tensor or placeholder for target. init_mean: the mean value to use for initialization. init_stddev: the standard devation to use for initialization. Returns: Predictions and loss tensors. Side effects: The variables linear_regression.weights and linear_regression.bias are initialized as follows. If init_mean is not None, then initialization will be done using a random normal initializer with the given init_mean and init_stddv. (These may be set to 0.0 each if a zero initialization is desirable for convex use cases.) If init_mean is None, then the uniform_unit_scaling_initialzer will be used. """ with vs.variable_scope('linear_regression'): scope_name = vs.get_variable_scope().name logging_ops.histogram_summary('%s.x' % scope_name, x) logging_ops.histogram_summary('%s.y' % scope_name, y) dtype = x.dtype.base_dtype y_shape = y.get_shape() if len(y_shape) == 1: output_shape = 1 else: output_shape = y_shape[1] # Set up the requested initialization. if init_mean is None: weights = vs.get_variable( 'weights', [x.get_shape()[1], output_shape], dtype=dtype) bias = vs.get_variable('bias', [output_shape], dtype=dtype) else: weights = vs.get_variable('weights', [x.get_shape()[1], output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) bias = vs.get_variable('bias', [output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) logging_ops.histogram_summary('%s.weights' % scope_name, weights) logging_ops.histogram_summary('%s.bias' % scope_name, bias) return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
def linear_regression(x, y, init_mean=None, init_stddev=1.0): """Creates linear regression TensorFlow subgraph. Args: x: tensor or placeholder for input features. y: tensor or placeholder for labels. init_mean: the mean value to use for initialization. init_stddev: the standard devation to use for initialization. Returns: Predictions and loss tensors. Side effects: The variables linear_regression.weights and linear_regression.bias are initialized as follows. If init_mean is not None, then initialization will be done using a random normal initializer with the given init_mean and init_stddv. (These may be set to 0.0 each if a zero initialization is desirable for convex use cases.) If init_mean is None, then the uniform_unit_scaling_initialzer will be used. """ with vs.variable_scope('linear_regression'): scope_name = vs.get_variable_scope().name summary.histogram('%s.x' % scope_name, x) summary.histogram('%s.y' % scope_name, y) dtype = x.dtype.base_dtype y_shape = y.get_shape() if len(y_shape) == 1: output_shape = 1 else: output_shape = y_shape[1] # Set up the requested initialization. if init_mean is None: weights = vs.get_variable( 'weights', [x.get_shape()[1], output_shape], dtype=dtype) bias = vs.get_variable('bias', [output_shape], dtype=dtype) else: weights = vs.get_variable('weights', [x.get_shape()[1], output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) bias = vs.get_variable('bias', [output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) summary.histogram('%s.weights' % scope_name, weights) summary.histogram('%s.bias' % scope_name, bias) return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
def linear_regression(x, y, init_mean=None, init_stddev=1.0): """Creates linear regression TensorFlow subgraph. Args: x: tensor or placeholder for input features. y: tensor or placeholder for labels. init_mean: the mean value to use for initialization. init_stddev: the standard devation to use for initialization. Returns: Predictions and loss tensors. Side effects: The variables linear_regression.weights and linear_regression.bias are initialized as follows. If init_mean is not None, then initialization will be done using a random normal initializer with the given init_mean and init_stddv. (These may be set to 0.0 each if a zero initialization is desirable for convex use cases.) If init_mean is None, then the uniform_unit_scaling_initialzer will be used. """ with vs.variable_scope('linear_regression'): scope_name = vs.get_variable_scope().name summary.histogram('%s.x' % scope_name, x) summary.histogram('%s.y' % scope_name, y) dtype = x.dtype.base_dtype y_shape = y.get_shape() if len(y_shape) == 1: output_shape = 1 else: output_shape = y_shape[1] # Set up the requested initialization. if init_mean is None: weights = vs.get_variable( 'weights', [x.get_shape()[1], output_shape], dtype=dtype) bias = vs.get_variable('bias', [output_shape], dtype=dtype) else: weights = vs.get_variable( 'weights', [x.get_shape()[1], output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) bias = vs.get_variable( 'bias', [output_shape], initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) summary.histogram('%s.weights' % scope_name, weights) summary.histogram('%s.bias' % scope_name, bias) return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
