我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用theano.tensor.nnet()。
def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): """Apply batch normalization on x given mean, var, beta and gamma. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_test'): return _old_batch_normalization(x, mean, var, beta, gamma, epsilon) if mean.ndim == 1: # based on TensorFlow's default: normalize along rightmost dimension reduction_axes = range(x.ndim - 1) else: reduction_axes = [i for i in range(x.ndim) if mean.broadcastable[i]] return T.nnet.bn.batch_normalization_test( x, gamma, beta, mean, var, reduction_axes, epsilon) # TODO remove this function when Theano without # T.nnet.bn.batch_normalization_train is deprecated
def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): '''Apply batch normalization on x given mean, var, beta and gamma. ''' # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_test'): return _old_batch_normalization(x, mean, var, beta, gamma, epsilon) if mean.ndim == 1: # based on TensorFlow's default: normalize along rightmost dimension reduction_axes = range(x.ndim - 1) else: reduction_axes = [i for i in range(x.ndim) if mean.broadcastable[i]] return T.nnet.bn.batch_normalization_test( x, gamma, beta, mean, var, reduction_axes, epsilon) # TODO remove this function when Theano without # T.nnet.bn.batch_normalization_train is deprecated
def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_train is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_train'): return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon) if gamma is None: if beta is None: gamma = ones_like(x) else: gamma = ones_like(beta) if beta is None: if gamma is None: beta = zeros_like(x) beta = zeros_like(gamma) normed, mean, stdinv = T.nnet.bn.batch_normalization_train( x, gamma, beta, reduction_axes, epsilon) return normed, mean, T.inv(stdinv ** 2)
def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): """Apply batch normalization on x given mean, var, beta and gamma. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_test'): return _old_batch_normalization(x, mean, var, beta, gamma, epsilon) if gamma is None: gamma = ones_like(var) if beta is None: beta = zeros_like(mean) if mean.ndim == 1: # based on TensorFlow's default: normalize along rightmost dimension reduction_axes = list(range(x.ndim - 1)) else: reduction_axes = [i for i in range(x.ndim) if mean.broadcastable[i]] return T.nnet.bn.batch_normalization_test( x, gamma, beta, mean, var, reduction_axes, epsilon) # TODO remove this function when Theano without # T.nnet.bn.batch_normalization_train is deprecated
def batch_normalization(x, mean, var, beta, gamma, epsilon=0.0001): '''Apply batch normalization on x given mean, var, beta and gamma. ''' ndim = x.ndim dev = theano.config.device use_cudnn = ndim < 5 and (dev.startswith('cuda') or dev.startswith('gpu')) if use_cudnn: try: axis = mean.broadcastable.index(False) if axis != 1: shuffle_pattern = list(range(ndim)) shuffle_pattern[1] = shuffle_pattern[axis] shuffle_pattern[axis] = 1 x = x.dimshuffle(shuffle_pattern) mean = mean.dimshuffle(shuffle_pattern) var = var.dimshuffle(shuffle_pattern) beta = beta.dimshuffle(shuffle_pattern) gamma = gamma.dimshuffle(shuffle_pattern) normed = theano.sandbox.cuda.dnn.dnn_batch_normalization_test(x, gamma, beta, mean, var, 'spatial', epsilon) if axis != 1: normed = normed.dimshuffle(shuffle_pattern) return normed except AttributeError: pass except ValueError: pass return T.nnet.bn.batch_normalization(x, gamma, beta, mean, sqrt(var + epsilon), mode='high_mem') # SHAPE OPERATIONS
def elu(x, alpha=1.0): """ Exponential linear unit # Arguments x: Tensor to compute the activation function for. alpha: scalar """ _assert_has_capability(T.nnet, 'elu') return T.nnet.elu(x, alpha)
def relu(x, alpha=0., max_value=None): _assert_has_capability(T.nnet, 'relu') x = T.nnet.relu(x, alpha) if max_value is not None: x = T.minimum(x, max_value) return x
def softmax(x): return T.nnet.softmax(x)
def softplus(x): return T.nnet.softplus(x)
def binary_crossentropy(output, target, from_logits=False): if from_logits: output = T.nnet.sigmoid(output) # avoid numerical instability with _EPSILON clipping output = T.clip(output, _EPSILON, 1.0 - _EPSILON) return T.nnet.binary_crossentropy(output, target)
def sigmoid(x): return T.nnet.sigmoid(x)
def hard_sigmoid(x): return T.nnet.hard_sigmoid(x)
def deconv2d(x, kernel, output_shape, strides=(1, 1), border_mode='valid', dim_ordering='default', image_shape=None, filter_shape=None): '''2D deconvolution (transposed convolution). # Arguments kernel: kernel tensor. output_shape: desired dimensions of output. strides: strides tuple. border_mode: string, "same" or "valid". dim_ordering: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering in inputs/kernels/ouputs. ''' flip_filters = False if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise Exception('Unknown dim_ordering ' + str(dim_ordering)) x = _preprocess_conv2d_input(x, dim_ordering) kernel = _preprocess_conv2d_kernel(kernel, dim_ordering) kernel = kernel.dimshuffle((1, 0, 2, 3)) th_border_mode = _preprocess_border_mode(border_mode) np_kernel = kernel.eval() filter_shape = _preprocess_conv2d_filter_shape(dim_ordering, filter_shape) op = T.nnet.abstract_conv.AbstractConv2d_gradInputs(imshp=output_shape, kshp=filter_shape, subsample=strides, border_mode=th_border_mode, filter_flip=not flip_filters) conv_out = op(kernel, x, output_shape[2:]) conv_out = _postprocess_conv2d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering) return conv_out
def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. """ # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_train is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_train'): return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon) normed, mean, stdinv = T.nnet.bn.batch_normalization_train( x, gamma, beta, reduction_axes, epsilon) return normed, mean, T.inv(stdinv ** 2)
def categorical_crossentropy(output, target, from_logits=False): if from_logits: output = T.nnet.softmax(output) else: # scale preds so that the class probas of each sample sum to 1 output /= output.sum(axis=-1, keepdims=True) # avoid numerical instability with _EPSILON clipping output = T.clip(output, _EPSILON, 1.0 - _EPSILON) return T.nnet.categorical_crossentropy(output, target)
def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): '''Computes mean and std for batch then apply batch_normalization on batch. ''' # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_train is deprecated if not hasattr(T.nnet.bn, 'batch_normalization_train'): return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon) normed, mean, stdinv = T.nnet.bn.batch_normalization_train( x, gamma, beta, reduction_axes, epsilon) return normed, mean, T.inv(stdinv ** 2)
def _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): '''Computes mean and std for batch then apply batch_normalization on batch. ''' dev = theano.config.device use_cudnn = ndim(x) < 5 and reduction_axes == [0, 2, 3] and (dev.startswith('cuda') or dev.startswith('gpu')) if use_cudnn: broadcast_beta = beta.dimshuffle('x', 0, 'x', 'x') broadcast_gamma = gamma.dimshuffle('x', 0, 'x', 'x') try: normed, mean, stdinv = theano.sandbox.cuda.dnn.dnn_batch_normalization_train( x, broadcast_gamma, broadcast_beta, 'spatial', epsilon) var = T.inv(stdinv ** 2) return normed, T.flatten(mean), T.flatten(var) except AttributeError: pass var = x.var(reduction_axes) mean = x.mean(reduction_axes) target_shape = [] for axis in range(ndim(x)): if axis in reduction_axes: target_shape.append(1) else: target_shape.append(x.shape[axis]) target_shape = T.stack(*target_shape) broadcast_mean = T.reshape(mean, target_shape) broadcast_var = T.reshape(var, target_shape) broadcast_beta = T.reshape(beta, target_shape) broadcast_gamma = T.reshape(gamma, target_shape) normed = batch_normalization(x, broadcast_mean, broadcast_var, broadcast_beta, broadcast_gamma, epsilon) return normed, mean, var # TODO remove this if statement when Theano without # T.nnet.bn.batch_normalization_test is deprecated
def deconv2d(x, kernel, output_shape, strides=(1, 1), border_mode='valid', dim_ordering='default', image_shape=None, filter_shape=None): '''2D deconvolution (transposed convolution). # Arguments kernel: kernel tensor. output_shape: desired dimensions of output. strides: strides tuple. border_mode: string, "same" or "valid". dim_ordering: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering in inputs/kernels/ouputs. ''' flip_filters = False if dim_ordering == 'default': dim_ordering = image_dim_ordering() if dim_ordering not in {'th', 'tf'}: raise ValueError('Unknown dim_ordering ' + dim_ordering) x = _preprocess_conv2d_input(x, dim_ordering) kernel = _preprocess_conv2d_kernel(kernel, dim_ordering) kernel = kernel.dimshuffle((1, 0, 2, 3)) th_border_mode = _preprocess_border_mode(border_mode) np_kernel = kernel.eval() filter_shape = _preprocess_conv2d_filter_shape(dim_ordering, filter_shape) filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3)) op = T.nnet.abstract_conv.AbstractConv2d_gradInputs(imshp=output_shape, kshp=filter_shape, subsample=strides, border_mode=th_border_mode, filter_flip=not flip_filters) conv_out = op(kernel, x, output_shape[2:]) conv_out = _postprocess_conv2d_output(conv_out, x, border_mode, np_kernel, strides, dim_ordering) return conv_out
def relu(x, alpha=0., max_value=None): assert hasattr(T.nnet, 'relu'), ('It looks like like your version of ' 'Theano is out of date. ' 'Install the latest version with:\n' 'pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps') x = T.nnet.relu(x, alpha) if max_value is not None: x = T.minimum(x, max_value) return x
def relu(x): return tensor.nnet.relu(x)
def relu(x): return tensor.switch(x > 0, x, 0) #def relu(x): # return tensor.nnet.relu(x) # #return tensor.switch(x > upper, upper, tensor.nnet.relu(x))
def __init__(self, rng, f='ReLU', g=lambda x: x, params=None): if f == 'ReLU': if hasattr(T.nnet, 'relu'): self.f = T.nnet.relu else: self.f = lambda x: T.switch(x<0,0,x) self.g = lambda x: x elif f == 'PReLU': # Avoids dying ReLU units if hasattr(T.nnet, 'relu'): self.f = lambda x: T.nnet.relu(x, alpha=0.01) else: self.f = lambda x: T.switch(x<=0,a*x,x) self.g = lambda x: x elif f == 'tanh': self.f = T.tanh self.g = T.arctanh elif f == 'sigmoid': self.f = T.nnet.sigmoid self.g = lambda x: x elif f == 'softmax': self.f = T.nnet.softmax self.g = lambda x: x elif f == 'softplus': self.f = T.nnet.softplus self.g = lambda x: x elif f == 'identity': self.f = lambda x: x self.g = lambda x: x else: self.f = f self.g = g self.params = [] if params is None else params
