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本篇内容主要讲解“numpy.float32怎么使用”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“numpy.float32怎么使用”吧!
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
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
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]))
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
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(anchors.shape[0]) return anchors, length
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.int64]
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 miximport 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.float32 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 daimport numpy as np from numpy import 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]
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, maskimport 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
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 = [] 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
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
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 updatesimport 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
if param in constraints:
c=constraints[param];
new_p=c(new_p);
updates.append((param, new_p))
return updatesimport 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))
到此，相信大家对“numpy.float32怎么使用”有了更深的了解，不妨来实际操作一番吧！这里是亿速云网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
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