Python numpy.zeros函数代码示例

def main(args):
    # Create model.
    model = RNNNet(args)
    for k, v in model.param_configs.items():
        model.params[k] = np.zeros(v['shape'])

    data = np.zeros((args.batch_size, args.input_size)) # Data of only one time step.
    label = np.zeros((args.batch_size,), dtype=np.int)

    for l in range(args.num_loops):
        if l == num_cold:
            start = time.time()
        def loss_func(*params):
            f = model.forward(data, 'train')
            return model.loss(f, label)
        if args.only_forward:
            loss = loss_func()
            loss.asnumpy()
        else:
            param_arrays = list(model.params.values())
            param_keys = list(model.params.keys())
            grad_and_loss_func = core.grad_and_loss(
                loss_func, argnum=range(len(param_arrays)))
            grad_arrays, loss = grad_and_loss_func(*param_arrays)
    dur = time.time() - start
    print('Per Loop Time: %.6f' % (dur / (args.num_loops - num_cold)))

def lstm_temporal(x, h0, Wx, Wh, b):
    """
    Forward pass for an LSTM over an entire sequence of data. We assume an input
    sequence composed of T vectors, each of dimension D. The LSTM uses a hidden
    size of H, and we work over a minibatch containing N sequences. After running
    the LSTM forward, we return the hidden states for all timesteps.

    Note that the initial cell state is passed as input, but the initial cell
    state is set to zero. Also note that the cell state is not returned; it is
    an internal variable to the LSTM and is not accessed from outside.

    Inputs:
    - x: Input data of shape (N, T, D)
    - h0: Initial hidden state of shape (N, H)
    - Wx: Weights for input-to-hidden connections, of shape (D, 4H)
    - Wh: Weights for hidden-to-hidden connections, of shape (H, 4H)
    - b: Biases of shape (4H,)

    Returns a tuple of:
    - h: Hidden states for all timesteps of all sequences, of shape (N, T, H)
    """
    N, T, D = x.shape
    _, H = h0.shape
    c = np.zeros([N, 0, H])
    h = np.zeros([N, 0, H])
    for t in xrange(T):
        h_step, c_step = lstm_step(
          x[:, t, :], h[:, t-1, :] if t > 0 else h0, c[:, t-1, :] if t > 0 else np.zeros((N, H)), Wx, Wh, b)
        h_step = h_step.reshape(N, 1, H)
        c_step = c_step.reshape(N, 1, H)
        h = np.append(h, h_step, axis=1)
        c = np.append(c, c_step, axis=1)
    return h

    def __init__(self,
                 input_dim=3 * 32 * 32,
                 hidden_dim=100,
                 num_classes=10,
                 weight_scale=1e-3,
                 reg=0.0,
                 conv_mode='lazy',
                 dtype=py_np.float64):
        """
        Initialize a new network.

        Inputs:
        - input_dim: An integer giving the size of the input
        - hidden_dim: An integer giving the size of the hidden layer
        - num_classes: An integer giving the number of classes to classify
        - dropout: Scalar between 0 and 1 giving dropout strength.
        - weight_scale: Scalar giving the standard deviation for random
          initialization of the weights.
        - reg: Scalar giving L2 regularization strength.
        """
        super(TwoLayerNet, self).__init__(conv_mode)
        self.params = {}
        self.reg = reg

        self.params['W1'] = random.randn(input_dim, hidden_dim) * weight_scale
        self.params['b1'] = np.zeros((hidden_dim))
        self.params['W2'] = random.randn(hidden_dim, num_classes) * weight_scale
        self.params['b2'] = np.zeros((num_classes))

def main(args):
    # Create model.
    model = TwoLayerNet(args)
    for k, v in model.param_configs.items():
        model.params[k] = np.zeros(v['shape'])

    img = np.zeros((args.batch_size, 784))
    label = np.zeros((args.batch_size,))

    start = time.time()
    for l in range(num_loops):
        def loss_func(*params):
            f = model.forward(img, 'train')
            return model.loss(f, label)
        if args.only_forward:
            loss_func()
            loss.asnumpy()
        else:
            param_arrays = list(model.params.values())
            param_keys = list(model.params.keys())
            grad_and_loss_func = minpy.core.grad_and_loss(
                loss_func, argnum=range(len(param_arrays)))
            grad_arrays, loss = grad_and_loss_func(*param_arrays)
            for g in grad_arrays:
                g.get_data(minpy.array_variants.ArrayType.MXNET).wait_to_read()
    dur = time.time() - start
    print('Per Loop Time: %.6f' % (dur / num_loops))

 def gaussian_cluster_generator(num_samples=10000, num_features=500, num_classes=5):
     mu = np.random.rand(num_classes, num_features)
     sigma = np.ones((num_classes, num_features)) * 0.1
     num_cls_samples = int(num_samples / num_classes)
     x = np.zeros((num_samples, num_features))
     y = np.zeros((num_samples, num_classes))
     for i in range(num_classes):
         cls_samples = np.random.normal(mu[i,:], sigma[i,:], (num_cls_samples, num_features))
         x[i*num_cls_samples:(i+1)*num_cls_samples] = cls_samples
         y[i*num_cls_samples:(i+1)*num_cls_samples,i] = 1
     return x, y

 def forward(self, X, mode):
     seq_len = X.shape[1]
     batch_size = X.shape[0]
     hidden_size = self.params['Wh'].shape[0]
     h = np.zeros((batch_size, hidden_size))
     c = np.zeros((batch_size, hidden_size))
     for t in xrange(seq_len):
         h, c = layers.lstm_step(X[:, t, :], h, c, self.params['Wx'], self.params['Wh'],
                                 self.params['b'])
     y = layers.affine(h, self.params['Wa'], self.params['ba'])
     return y

def softmax_cross_entropy(prob, label):
    """
    Computes the cross entropy for softmax activation.

    Inputs:
    - prob: Probability, of shape (N, C) where x[i, j] is the probability for the jth class
      for the ith input.
    - label: Either of the followings:
      - One hot encoding of labels, of shape (N, C)
      - Label index of shape (N, ), each y[i] is the label of i^th example
        (0 <= y[i] < C)

    Returns a Value:
    - cross_entropy
    """

    N = prob.shape[0]
    C = prob.shape[1]
    if len(label.shape) == 1:
        #convert it to one hot encoding
        onehot_label = np.zeros([N, C])
        np.onehot_encode(label, onehot_label)
    else:
        onehot_label = label
    return -np.sum(np.log(prob) * onehot_label) / N

def rnn_temporal(x, h0, Wx, Wh, b):
    """
    Run a vanilla RNN forward on an entire sequence of data. We assume an input
    sequence composed of T vectors, each of dimension D. The RNN uses a hidden
    size of H, and we work over a minibatch containing N sequences. After running
    the RNN forward, we return the hidden states for all timesteps.

    Inputs:
    - x: Input data for the entire timeseries, of shape (N, T, D).
    - h0: Initial hidden state, of shape (N, H)
    - Wx: Weight matrix for input-to-hidden connections, of shape (D, H)
    - Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H)
    - b: Biases of shape (H,)

    Returns a tuple of:
    - h: Hidden states for the entire timeseries, of shape (N, T, H).
    """
    N, T, _ = x.shape
    H = h0.shape[1]
    h = np.zeros([N, 0, H])
    for t in range(T):
        h_step = rnn_step(x[:, t, :], h0 if t == 0 else h[:, t - 1, :], Wx, Wh,
                          b).reshape(N, 1, H)
        h = np.append(h, h_step, axis=1)
    return h

 def forward(self, X, mode):
     h = np.zeros(self.hshape)  # init hidden state
     for t in xrange(self.num_unroll_steps):
         h = layers.rnn_step(X, h, self.params['Wx'],
                             self.params['Wh'], self.params['b'])
     y = layers.affine(h, self.params['Wa'], self.params['ba'])
     return y

def softmax_loss(x, y):
    """
    Computes the loss and gradient for softmax classification.

    Inputs:
    - x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
      for the ith input.
    - y: Either of the followings:
      - One hot encoding of labels, of shape (N, C)
      - Label index of shape (N, ), each y[i] is the label of i^th example
        (0 <= y[i] < C)

    Returns a tuple of:
    - loss: Scalar giving the loss
    """
    N = x.shape[0]
    C = x.shape[1]
    if len(y.shape) == 1:
        #convert it to one hot encoding
        onehot_y = np.zeros([N, C])
        np.onehot_encode(y, onehot_y)
    else:
        onehot_y = y
    probs = x - np.max(x, axis=1, keepdims=True)
    loss = -np.sum(probs * onehot_y) / N
    loss += np.sum(np.log(np.sum(np.exp(probs), axis=1, keepdims=True))) / N
    return loss

def test_lr_grad():
    def sigmoid(x):
        return 0.5 * (np.tanh(x / 2) + 1)

    def predict(weights, inputs):
        return sigmoid(np.dot(inputs, weights))

    def training_loss(inputs):
        preds = predict(weights, inputs)
        label_probabilities = preds * targets + (1 - preds) * (1 - targets)
        l = -np.sum(np.log(label_probabilities))
        return l

    def training_accuracy(weights, inputs):
        preds = predict(weights, inputs)
        error = np.count_nonzero(np.argmax(preds, axis=1) - np.argmax(targets, axis=1))
        return (256 - error) * 100 / 256.0

    wshape = (500, 250)
    weights = random.rand(*wshape) - 0.5

    xshape = (256, 500)
    tshape = (256, 250)
    inputs = random.rand(*xshape) - 0.5
    targets = np.zeros(tshape)
    truth = random.randint(0, 250, 256)
    targets[np.arange(256), truth] = 1

    gradient_checker.quick_grad_check(training_loss, inputs)

def test_context():
    set_context(gpu(1)) # set the global context as gpu(1)
    
    def sigmoid(x):
        return 0.5 * (np.tanh(x / 2) + 1)
    
    def predict(weights, inputs):
        return sigmoid(np.dot(inputs, weights))
    
    def training_loss(weights, inputs):
        preds = predict(weights, inputs)
        label_probabilities = preds * targets + (1 - preds) * (1 - targets)
        l = -np.sum(np.log(label_probabilities))
        return l
    
    def training_accuracy(weights, inputs):
        preds = predict(weights, inputs)
        error = np.count_nonzero(np.argmax(preds, axis=1) - np.argmax(targets, axis=1))
        return (256 - error) * 100 / 256.0
    
    with gpu(0):
        xshape = (256, 500)
        wshape = (500, 250)
        tshape = (256, 250)
        inputs = random.rand(*xshape) - 0.5
        targets = np.zeros(tshape)
        truth = random.randint(0, 250, 256)
        targets[np.arange(256), truth] = 1
        weights = random.rand(*wshape) - 0.5
    
        training_gradient_fun = grad(training_loss)
    
        for i in range(20):
            print('Trained loss accuracy #{}: {}%'.format(i, training_accuracy(weights, inputs)))
            gr = training_gradient_fun(weights, inputs)
            weights -= gr * 0.01
        print("\nff and bp on {0}".format(weights.context))
    
    print("\nexecute on cpu")
    with cpu():
        x_cpu = random.rand(32, 64) - 0.5
        y_cpu = random.rand(64, 32) - 0.5
        z_cpu = np.dot(x_cpu, y_cpu)
        print('z_cpu.context = {0}'.format(z_cpu.context))
    
    print("\nexecute on gpu(0)")
    with gpu(0):
        x_gpu0 = random.rand(32, 64) - 0.5
        y_gpu0 = random.rand(64, 32) - 0.5
        z_gpu0 = np.dot(x_gpu0, y_gpu0)
        z_gpu0.asnumpy()
        print('z_gpu0.context = {0}'.format(z_gpu0.context))
    
    print("\n[use global context] execute on gpu(1)")
    x_gpu1 = random.rand(32, 64) - 0.5
    y_gpu1 = random.rand(64, 32) - 0.5
    z_gpu1 = np.dot(x_gpu1, y_gpu1)
    z_gpu1.asnumpy()
    print('z_gpu1.context = {0}'.format(z_gpu1.context))

 def forward(self, X, mode):
     out = self.conv(X=X, **self.params)
     out = layers.affine(out, self.params['w1'], self.params['b1'])
     out = layers.relu(out)
     out = layers.affine(out, self.params['w2'], self.params['b2'])
     # This verifies whether symbols can be reused.
     trash = self.conv(X=np.zeros(X.shape), **self.params)
     return out

    def set_param(self):
        self.params = {}

        c_cnt, height, width = self.input_dim
        f_cnt = self.num_filters
        f_h, f_w = self.filter_size, self.filter_size

        self.params['conv1_weight'] = random.randn(f_cnt, c_cnt, f_h,
                                                   f_w) * self.weight_scale
        self.params['conv1_bias'] = np.zeros(f_cnt)

        #TODO(Haoran): whole stuff about all dimension calculations
        #should be substituted by quering symbol.arg_list
        conv_stride = 1
        conv_pad = (f_h - 1) / 2

        Hc, Wc = 1 + (height + 2 * conv_pad - f_h) / conv_stride, 1 + (
            width + 2 * conv_pad - f_w) / conv_stride

        pool_height, pool_width = 2, 2
        pool_stride = 2

        Hp, Wp = (Hc - pool_height) / pool_stride + 1, (Wc - pool_width
                                                       ) / pool_stride + 1

        # weight has to be tranposed to fit mxnet's symbol
        self.params['fc1_weight'] = np.transpose(random.randn(
            5408, self.hidden_dim) * self.weight_scale)
        self.params['fc1_bias'] = np.zeros((self.hidden_dim))

        # weight has to be tranposed to fit mxnet's symbol
        self.params['fc2_weight'] = np.transpose(random.randn(
            self.hidden_dim, self.num_classes) * self.weight_scale)
        self.params['fc2_bias'] = np.zeros((self.num_classes))

        #TODO(Haoran): move following into parent structured model class
        self.param_keys = self.params.keys()

        # Build key's index in loss func's arglist
        self.key_args_index = {}
        for i, key in enumerate(self.param_keys):
            # data, targets would be the first two elments in arglist
            self.key_args_index[key] = self.data_target_cnt + i

def softmax_cross_entropy(prob, label):
    N = prob.shape[0]
    C = prob.shape[1]
    if len(label.shape) == 1:
        #convert it to one hot encoding
        onehot_label = np.zeros([N, C])
        np.onehot_encode(label, onehot_label)
    else:
        onehot_label = label
    return -np.sum(np.log(prob) * onehot_label) / N