numpy.float32的典型用法

目录

• 示例1：draw_image
• 示例2：generate_moving_mnist
• 示例3：wav_format
• 示例4：get_rois_blob
• 示例5：generate_anchors_pre
• 示例6：draw_heatmap
• 示例7：maybe_cast_to_float64
• 示例8：in_top_k
• 示例9：ctc_path_probs
• 示例10：rmsprop
• 示例11：adadelta
• 示例12：adagrad
• 示例13：sgd
• 示例14：sgdmomentum
• 示例15：set_values

本文汇总了python中numpy.float32方法的典型用法代码示例，可以为大家提供其具体用法示例。

示例1：draw_image

import numpy as np
from numpy import float32

def draw_image(self, img, color=[0, 255, 0], alpha=1.0, copy=True, from_img=None):
    if copy:
      img = np.copy(img)

    orig_dtype = img.dtype
    if alpha != 1.0 and img.dtype != np.float32:
      img = img.astype(np.float32, copy=False)

    for rect in self:
      if from_img is not None:
        rect.resize(from_img, img).draw_on_image(img, color=color, alpha=alpha, copy=False)
      else:
        rect.draw_on_image(img, color=color, alpha=alpha, copy=False)

    if orig_dtype != img.dtype:
      img = img.astype(orig_dtype, copy=False)

    return img

示例2：generate_moving_mnist

import numpy as np
from numpy import float32

def generate_moving_mnist(self, num_digits=2):
  '''
  Get random trajectories for the digits and generate a video.
  '''
  data = np.zeros((self.n_frames_total, self.image_size_, self.image_size_), dtype=np.float32)
  for n in range(num_digits):
   # Trajectory
   start_y, start_x = self.get_random_trajectory(self.n_frames_total)
   ind = random.randint(0, self.mnist.shape[0] - 1)
   digit_image = self.mnist[ind]
   for i in range(self.n_frames_total):
    top  = start_y[i]
    left  = start_x[i]
    bottom = top + self.digit_size_
    right = left + self.digit_size_
    # Draw digit
    data[i, top:bottom, left:right] = np.maximum(data[i, top:bottom, left:right], digit_image)

  data = data[..., np.newaxis]
  return data

示例3：wav_format

import numpy as np
from numpy import float32

def wav_format(self, input_wave_file, output_wave_file, target_phrase):
    pop_size = 100
    elite_size = 10
    mutation_p = 0.005
    noise_stdev = 40
    noise_threshold = 1
    mu = 0.9
    alpha = 0.001
    max_iters = 3000
    num_points_estimate = 100
    delta_for_gradient = 100
    delta_for_perturBATion = 1e3
    input_audio = load_wav(input_wave_file).astype(np.float32)
    pop = np.expand_dims(input_audio, axis=0)
    pop = np.tile(pop, (pop_size, 1))
    output_wave_file = output_wave_file
    target_phrase = target_phrase
    funcs = setup_graph(pop, np.array([toks.index(x) for x in target_phrase]))

示例4：get_rois_blob

import numpy as np
from numpy import float32

def get_rois_blob(im_rois, im_scale_factors):
  """Converts RoIs into network inputs.
  Arguments:
    im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
    im_scale_factors (list): scale factors as returned by _get_image_blob
  Returns:
    blob (ndarray): R x 5 matrix of RoIs in the image pyramid
  """
  rois_blob_real = []

  for i in range(len(im_scale_factors)):
    rois, levels = _project_im_rois(im_rois, np.array([im_scale_factors[i]]))
    rois_blob = np.hstack((levels, rois))
    rois_blob_real.append(rois_blob.astype(np.float32, copy=False))

  return rois_blob_real

示例5：generate_anchors_pre

import numpy as np
from numpy import float32

def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratIOS=(0.5,1,2)):
 """ A wrapper function to generate anchors given different scales
  Also return the number of anchors in variable 'length'
 """
 anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
 A = anchors.shape[0]
 shift_x = np.arange(0, width) * feat_stride
 shift_y = np.arange(0, height) * feat_stride
 shift_x, shift_y = np.meshgrid(shift_x, shift_y)
 shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
 K = shifts.shape[0]
 # width changes faster, so here it is H, W, C
 anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
 anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
 length = np.int32(an开发者_Js入门chors.shape[0])

 return anchors, length

示例6：draw_heatmap

import numpy as np
from numpy import float32

def draw_heatmap(img, heatmap, alpha=0.5):
  """Draw a heatmap overlay over an image."""
  assert len(heatmap.shape) == 2 or \
    (len(heatmap.shape) == 3 and heatmap.shape[2] == 1)
  assert img.dtype in [np.uint8, np.int32, np.int6python4]
  assert heatmap.dtype in [np.float32, np.float64]

  if img.shape[0:2] != heatmap.shape[0:2]:
    heatmap_rs = np.clip(heatmap * 255, 0, 255).astype(np.uint8)
    heatmap_rs = ia.imresize_single_image(
      heatmap_rs[..., np.newaxis],
      img.shape[0:2],
      interpolation="nearest"
    )
    heatmap = np.squeeze(heatmap_rs) / 255.0

  cmap = plt.get_cmap('jet')
  heatmap_cmapped = cmap(heatmap)
  heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
  heatmap_cmapped = heatmap_cmapped * 255
  mix = (1-alpha) * img + alpha * heatmap_cmapped
  mix = np.clip(mix, 0, 255).astype(np.uint8)
  return mix

示例7：maybe_cast_to_float64

import numpy as np
from numpy import float32

def maybe_cast_to_float64(da):
  """Cast DataArrays to np.float64 if they are of type np.float32.

  Parameters
  ----------
  da : xr.DataArray
    Input DataArray

  Returns
  -------
  DataArray

  """
  if da.dtype == np.float32:
    logging.warning('Datapoints were stored using the np.float3python2 datatype.'
            'For accurate reduction operations using bottleneck, '
            'datapoints are being cast to the np.float64 datatype.'
            ' For more information see: https://github.com/pydata/'
            'xarray/issues/1346')
    return da.astype(np.float64)
  else:
    return da

示例8：in_top_k

import numpy as np
from numpy import 编程客栈float32

def in_top_k(predictions, targets, k):
  '''Returns whether the `targets` are in the top `k` `predictions`

  # Arguments
    predictions: A tensor of shape batch_size x classess and type float32.
    targets: A tensor of shape batch_size and type int32 or int64.
    k: An int, number of top elements to consider.

  # Returns
    A tensor of shape batch_size and type int. output_i is 1 if
    targets_i is within top-k values of predictions_i
  '''
  predictions_top_k = T.argsort(predictions)[:, -k:]
  result, _ = theano.map(lambda prediction, target: any(equal(prediction, target)), sequences=[predictions_top_k, targets]

示例9：ctc_path_probs

import numpy as np
from numpy import float32

def ctc_path_probs(predict, Y, alpha=1e-4):
  smoothed_predict = (1 - alpha) * predict[:, Y] + alpha * np.float32(1.) / Y.shape[0]
  L = T.log(smoothed_predict)
  zeros = T.zeros_like(L[0])
  log_first = zeros

  f_skip_idxs = ctc_create_skip_idxs(Y)
  b_skip_idxs = ctc_create_skip_idxs(Y[::-1]) # there should be a shortcut to calculating this

  def step(log_f_curr, log_b_curr, f_active, log_f_prev, b_active, log_b_prev):
    f_active_next, log_f_next = ctc_update_log_p(f_skip_idxs, zeros, f_active, log_f_curr, log_f_prev)
    b_active_next, log_b_next = ctc_update_log_p(b_skip_idxs, zeros, b_active, log_b_curr, log_b_prev)
    return f_active_next, log_f_next, b_active_next, log_b_next

  [f_active, log_f_probs, b_active, log_b_probs], _ = theano.scan(
    step, sequences=[L, L[::-1, ::-1]], outputs_info=[np.int32(1), log_first, np.int32(1), log_first])

  idxs = T.arange(L.shape[1]).dimshuffle('x', 0)
  mask = (idxs < f_active.dimshuffle(0, 'x')) & (idxs < b_active.dimshuffle(0, 'x'))[::-1, ::-1]
  log_probs = log_f_probs + log_b_probs[::-1, ::-1] - L
  return log_probs, mask

示例10：rmsprop

import numpy as np
from numpy import float32

def rmsprop(self, cost, params, lr=0.001, rho=0.9, eps=1e-6,consider_constant=None):
    """
    RMSProp.
    """
    lr = theano.shared(np.float32(lr).astype(floatX))

    gradients = self.get_gradients(cost, params,consider_constant)
    accumulators = [theano.shared(np.zeros_like(p.get_value()).astype(np.float32)) for p in params]

    updates = []

    for param, gradient, accumulator in zip(params, gradients, accumulators):
      new_accumulator = rho * accumulator + (1 - rho) * gradient ** 2
      updates.append((accumulator, new_accumulator))

      new_param = param - lr * gradient / T.sqrt(new_accumulator + eps)
      updates.append((param, new_param))

    return updates

示例11：adadelta

import numpy as np
from numpy import float32

def adadelta(self, cost, params, rho=0.95, epsilon=1e-6,consider_constant=None):
    """
    Adadelta. Based on:
    http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf
    """
    rho = theano.shared(np.float32(rho).astype(floatX))
    epsilon = theano.shared(np.float32(epsilon).astype(floatX))

    gradients = self.get_gradients(cost, params,consider_constant)
    accu_gradients = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]
    accu_deltas = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]

    updates =http://www.devze.com []
    for param, gradient, accu_gradient, accu_delta in zip(params, gradients, accu_gradients, accu_deltas):
      new_accu_gradient = rho * accu_gradient + (1. - rho) * gradient ** 2.
      delta_x = - T.sqrt((accu_delta + epsilon) / (new_accu_gradient + epsilon)) * gradient
      new_accu_delta = rho * accu_delta + (1. - rho) * delta_x ** 2.
      updates.append((accu_gradient, new_accu_gradient))
      updates.append((accu_delta, new_accu_delta))
      updates.append((param, param + delta_x))
    return updates

示例12：adagrad

import numpy as np
from numpy import float32

def adagrad(self, cost, params, lr=1.0, epsilon=1e-6,consider_constant=None):
    """
    Adagrad. Based on http://www.ark.cs.cmu.edu/cdyer/adagrad.pdf
    """
    lr = theano.shared(np.float32(lr).astype(floatX))
    epsilon = theano.shared(np.float32(epsilon).astype(floatX))

    gradients = self.get_gradients(cost, params,consider_constant)
    gsums = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]

    updates = []
    for param, gradient, gsum in zip(params, gradients, gsums):
      new_gsum = gsum + gradient ** 2.
      updates.append((gsum, new_gsum))
      updates.append((param, param - lr * gradient / (T.sqrt(gsum + epsilon))))
    return updates

示例13：sgd

import numpy as np
from numpy import float32

def sgd(self, cost, params,constraints={}, lr=0.01):
    """
    Stochatic gradient descent.
    """
    updates = []
   
    lr = theano.shared(np.float32(lr).astype(floatX))
    gradients = self.get_gradients(cost, params)
   
    for p, g in zip(params, gradients):
      v=-lr*g;
      new_p=p+v;
      # apply constraints
      if p in constraints:
        c=constraints[p];
        new_p=c(new_p);
      updates.append((p, new_p))

    return updates

示例14：sgdmomentum

import numpy as np
from numpy import float32

def sgdmomentum(self, cost, params,constraints={}, lr=0.01,consider_constant=None, momentum=0.):
    """
    Stochatic gradient descent with momentum. Momentum has to be in [0, 1)
    """
    # Check that the momentum is a correct value
    assert 0 <= momentum < 1

    lr = theano.shared(np.float32(lr).astype(floatX))
    momentum = theano.shared(np.float32(momentum).astype(floatX))

    gradients = self.get_gradients(cost, params)
    velocities = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]

    updates = []
    for param, gradient, velocity in zip(params, gradients, velocities):
      new_velocity = momentum * velocity - lr * gradient
      updates.append((velocity, new_velocity))
      new_p=param+new_velocity;
      # apply constraints
      ifphp param in constraints:
        c=constraints[param];
        new_p=c(new_p);
      updates.append((param, new_p))
    return updates

示例15：set_values

import numpy as np
from numpy import float32

def set_values(name, param, pretrained):
  """
  Initialize a network parameter with pretrained values.
  We check that sizes are compatible.
  """
  param_value = param.get_value()
  if pretrained.size != param_value.size:
    raise Exception(
      "Size mismatch for parameter %s. Expected %i, found %i."
      % (name, param_value.size, pretrained.size)
    )
  param.set_value(np.reshape(
    pretrained, param_value.shape
  ).astype(np.float32))

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