Python numpy.asscalar方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def predict(self, f, k=5, resize_mode='fill'):
        from keras.preprocessing import image
        from vergeml.img import resize_image

        filename = os.path.basename(f)

        if not os.path.exists(f):
            return dict(filename=filename, prediction=[])

        img = image.load_img(f)
        img = resize_image(img, self.image_size, self.image_size, 'antialias', resize_mode)

        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = self.preprocess_input(x)
        preds = self.model.predict(x)
        pred = self._decode(preds, top=k)[0]
        prediction=[dict(probability=np.asscalar(perc), label=klass) for _, klass, perc in pred]

        return dict(filename=filename, prediction=prediction) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def add(self, es, ta, ma=None):
    if ma is not None:
      raise Exception('mask is not implemented')
    es = es.ravel()
    ta = ta.ravel()
    if es.shape[0] != ta.shape[0]:
      raise Exception('invalid shape of es, or ta')
    if es.min() < 0 or es.max() > 1:
      raise Exception('estimate has wrong value range')
    ta_p = (ta == 1)
    ta_n = (ta == 0)
    es_p = es[ta_p]
    es_n = es[ta_n]
    for idx, wp in enumerate(self.thresholds):
      wp = np.asscalar(wp)
      self.tps[idx] += (es_p > wp).sum()
      self.fps[idx] += (es_n > wp).sum()
      self.fns[idx] += (es_p <= wp).sum()
      self.tns[idx] += (es_n <= wp).sum()
    self.n_pos += ta_p.sum()
    self.n_neg += ta_n.sum() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def accuracy(model):
    accuracy = []
    prefix = model.net.Proto().name
    for device in model._devices:
        accuracy.append(
            np.asscalar(workspace.FetchBlob("gpu_{}/{}_accuracy".format(device, prefix))))
    return np.average(accuracy)


# ## Part 11: Run Multi-GPU Training and Get Test Results
# You've come a long way. Now is the time to see it all pay off. Since you already ran ResNet once, you can glance at the code below and run it. The big difference this time is your model is parallelized! 
# 
# The additional components at the end deal with accuracy so you may want to dig into those specifics as a bonus task. You can try it again: just adjust the `num_epochs` value below, run the block, and see the results. You can also go back to Part 10 to reinitialize the model, and run this step again. (You may want to add `workspace.ResetWorkspace()` before you run the new models again.)
# 
# Go back and check the images/sec from when you ran single GPU. Note how you can scale up with a small amount of overhead. 
# 
# ### Task: How many GPUs would it take to train ImageNet in under a minute? 

# In[ ]:


# Start looping through epochs where we run the batches of images to cover the entire dataset
# Usually you would want to run a lot more epochs to increase your model's accuracy 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def __call__(self, wav, srate=16000, nbits=16):
        """ Add noise to clean wav """
        if isinstance(wav, torch.Tensor):
            wav = wav.numpy()
        noise_idx = np.random.choice(list(range(len(self.noises))), 1)
        sel_noise = self.noises[np.asscalar(noise_idx)]
        noise = sel_noise['data']
        snr = np.random.choice(self.snr_levels, 1)
        # print('Applying SNR: {} dB'.format(snr[0]))
        if wav.ndim > 1:
            wav = wav.reshape((-1,))
        noisy, noise_bound = self.addnoise_asl(wav, noise, srate,
                                               nbits, snr,
                                               do_IRS=self.do_IRS)
        # normalize to avoid clipping
        if np.max(noisy) >= 1 or np.min(noisy) < -1:
            small = 0.1
            while np.max(noisy) >= 1 or np.min(noisy) < -1:
                noisy = noisy / (1. + small)
                small = small + 0.1
        return torch.FloatTensor(noisy.astype(np.float32)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def bar2e(ex,ey,ep):
    """
    Compute the element stiffness matrix for two dimensional bar element.
    
    :param list ex: element x coordinates [x1, x2]
    :param list ey: element y coordinates [y1, y2]
    :param list ep: [E, A]: E - Young's modulus, A - Cross section area
    :return mat Ke: stiffness matrix, [4 x 4]
    """
    E=ep[0]
    A=ep[1]
    
    b = np.mat([[ex[1]-ex[0]],[ey[1]-ey[0]]])
    L = np.asscalar(np.sqrt(b.T*b))
    
    Kle = np.mat([[1.,-1.],[-1.,1.]])*E*A/L
    
    n = np.asarray(b.T/L).reshape(2,)
    
    G = np.mat([
        [n[0],n[1],0.,0.],
        [0.,0.,n[0],n[1]]
    ])
    
    return G.T*Kle*G 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def l(self, x, u, i, terminal=False):
        """Instantaneous cost function.

        Args:
            x: Current state [state_size].
            u: Current control [action_size]. None if terminal.
            i: Current time step.
            terminal: Compute terminal cost. Default: False.

        Returns:
            Instantaneous cost (scalar).
        """
        if terminal:
            z = np.hstack([x, i])
            return np.asscalar(self._l_terminal(*z))

        z = np.hstack([x, u, i])
        return np.asscalar(self._l(*z)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def test_parse(self):
        """Base test for the `dket.data.decode` function."""

        words = [1, 2, 3, 0]
        formula = [12, 23, 34, 45, 0]
        example = data.encode(words, formula)
        serialized = example.SerializeToString()
        words_t, sent_len_t, formula_t, form_len_t = data.parse(serialized)

        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            actual = sess.run([words_t, sent_len_t, formula_t, form_len_t])
        self.assertEqual(words, actual[0].tolist())
        self.assertEqual(len(words), np.asscalar(actual[1]))
        self.assertEqual(formula, actual[2].tolist())
        self.assertEqual(len(formula), np.asscalar(actual[3])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def set_cumulative_capacities(self, node):
        if node.l:
            self.set_cumulative_capacities(node.l)
        if node.r:
            self.set_cumulative_capacities(node.r)
        if node.parent:
            if node.name:
                elevdiff = node.parent.elev - self.dem[self.ws[node.level] == node.name]
                vol = abs(np.asscalar(elevdiff[elevdiff > 0].sum()) * self.x * self.y)
                node.vol = vol
            else:
                leaves = []
                self.enumerate_leaves(node, level=node.level, stack=leaves)
                mask = np.isin(self.ws[node.level], leaves)
                boundary = list(chain.from_iterable([self.b[node.level].setdefault(pair, [])
                                                        for pair in combinations(leaves, 2)]))
                mask.flat[boundary] = True
                elevdiff = node.parent.elev - self.dem[mask]
                vol = abs(np.asscalar(elevdiff[elevdiff > 0].sum()) * self.x * self.y)
                node.vol = vol 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def generate_storage_uncontrolled(self, ixes, **kwargs):
        storage_uncontrolled = {}
        depths = 4
        init_depths = 0.1
        storage_ends = [np.asscalar(self.endnodes[np.where(self.startnodes == ix)])
                        for ix in ixes]
        storage_uncontrolled['name'] = 'ST' + pd.Series(ixes).astype(str)
        storage_uncontrolled['elev'] = self.grid.view(self.dem).flat[storage_ends]
        storage_uncontrolled['ymax'] = self.channel_d.flat[ixes] + 1
        storage_uncontrolled['y0'] = 0
        storage_uncontrolled['Acurve'] = 'FUNCTIONAL'
        storage_uncontrolled['A0'] = self.channel_w.flat[ixes]
        storage_uncontrolled['A1'] = 0
        storage_uncontrolled['A2'] = 1
        storage_uncontrolled = pd.DataFrame.from_dict(storage_uncontrolled)
        # Manual overrides
        for key, value in kwargs.items():
            storage_uncontrolled[key] = value
        self.storage_uncontrolled = storage_uncontrolled[['name', 'elev', 'ymax', 'y0', 'Acurve',
                            'A1', 'A2', 'A0']] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def generate_storage_controlled(self, ixes, **kwargs):
        storage_controlled = {}
        depths = 2
        init_depths = 0.1
        storage_ends = [np.asscalar(self.endnodes[np.where(self.startnodes == ix)])
                        for ix in ixes]
        storage_controlled['name'] = 'C' + pd.Series(ixes).astype(str)
        storage_controlled['elev'] = self.grid.view(self.dem).flat[storage_ends]
        storage_controlled['ymax'] = depths
        storage_controlled['y0'] = 0
        storage_controlled['Acurve'] = 'FUNCTIONAL'
        storage_controlled['A0'] = 1000
        storage_controlled['A1'] = 10000
        storage_controlled['A2'] = 1
        storage_controlled = pd.DataFrame.from_dict(storage_controlled)
        # Manual overrides
        for key, value in kwargs.items():
            storage_controlled[key] = value
        self.storage_controlled = storage_controlled[['name', 'elev', 'ymax', 'y0', 'Acurve',
                            'A1', 'A2', 'A0']] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def __index__(self):
    """Returns a python scalar.

    This allows using an instance of this class as an array index.
    Note that only arrays of integer types with size 1 can be used as array
    indices.

    Returns:
      A Python scalar.

    Raises:
      TypeError: If the array is not of an integer type.
      ValueError: If the array does not have size 1.
    """
    # TODO(wangpeng): Handle graph mode
    return np.asscalar(self.data.numpy()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def end(self, session):  # pylint: disable=unused-argument
    """Runs evaluator for final model."""
    # Only runs eval at the end if highest accuracy so far
    # is less than self._stop_threshold.
    if not self._run_success:
      step = np.asscalar(session.run(self._global_step_tensor))
      logging.info('Starting eval.')
      eval_results = self._evaluate(session, step)
      mlperf_log.resnet_print(key=mlperf_log.EVAL_STOP)
      mlperf_log.resnet_print(
          key=mlperf_log.EVAL_ACCURACY,
          value={
              'epoch': max(step // self._steps_per_epoch - 1, 0),
              'value': float(eval_results[_EVAL_METRIC])
          })
      if eval_results[_EVAL_METRIC] >= self._stop_threshold:
        mlperf_log.resnet_print(
            key=mlperf_log.RUN_STOP, value={'success': 'true'})
      else:
        mlperf_log.resnet_print(
            key=mlperf_log.RUN_STOP, value={'success': 'false'}) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def _toscalar(v):
    if isinstance(v, (np.float16, np.float32, np.float64,
                      np.uint8, np.uint16, np.uint32, np.uint64,
                      np.int8, np.int16, np.int32, np.int64)):
        return np.asscalar(v)
    else:
        return v 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def get_intent(self, code_repr='label'):
        ''' Get intent code, parameters and name

        Parameters
        ----------
        code_repr : string
           string giving output form of intent code representation.
           Default is 'label'; use 'code' for integer representation.

        Returns
        -------
        code : string or integer
            intent code, or string describing code
        parameters : tuple
            parameters for the intent
        name : string
            intent name

        Examples
        --------
        >>> hdr = Nifti1Header()
        >>> hdr.set_intent('t test', (10,), name='some score')
        >>> hdr.get_intent()
        ('t test', (10.0,), 'some score')
        >>> hdr.get_intent('code')
        (3, (10.0,), 'some score')
        '''
        hdr = self._structarr
        recoder = self._field_recoders['intent_code']
        code = int(hdr['intent_code'])
        if code_repr == 'code':
            label = code
        elif code_repr == 'label':
            label = recoder.label[code]
        else:
            raise TypeError('repr can be "label" or "code"')
        n_params = len(recoder.parameters[code])
        params = (float(hdr['intent_p%d' % (i+1)]) for i in range(n_params))
        name = asstr(np.asscalar(hdr['intent_name']))
        return label, tuple(params), name 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import asscalar [as 別名]
def _chk_magic_offset(hdr, fix=False):
        rep = Report(HeaderDataError)
        # for ease of later string formatting, use scalar of byte string
        magic = np.asscalar(hdr['magic'])
        offset = hdr['vox_offset']
        if magic == asbytes('n+1'): # one file
            if offset >= 352:
                if not offset % 16:
                    return hdr, rep
                else:
                    # SPM uses memory mapping to read the data, and
                    # apparently this has to start on 16 byte boundaries
                    rep.problem_msg = ('vox offset (=%s) not divisible '
                                       'by 16, not SPM compatible' % offset)
                    rep.problem_level = 30
                    if fix:
                        rep.fix_msg = 'leaving at current value'
                    return hdr, rep
            rep.problem_level = 40
            rep.problem_msg = ('vox offset %d too low for '
                               'single file nifti1' % offset)
            if fix:
                hdr['vox_offset'] = 352
                rep.fix_msg = 'setting to minimum value of 352'
        elif magic != asbytes('ni1'): # two files
            # unrecognized nii magic string, oh dear
            rep.problem_msg = ('magic string "%s" is not valid' %
                               asstr(magic))
            rep.problem_level = 45
            if fix:
                rep.fix_msg = 'leaving as is, but future errors are likely'
        return hdr, rep