Simple activation function to be applied to the output.
Simple activation function to be applied to the output. Available activations: 'tanh', 'relu', 'sigmoid', 'softmax', 'softplus', 'softsign', 'hard_sigmoid', 'linear', 'relu6', 'tanh_shrink', 'softmin', 'log_sigmoid' and 'log_softmax'.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Add a (non-learnable) scalar constant to the input.
Add a (non-learnable) scalar constant to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies an atrous convolution operator for filtering neighborhoods of 1-D inputs.
Applies an atrous convolution operator for filtering neighborhoods of 1-D inputs. A.k.a dilated convolution or convolution with holes. Bias will be included in this layer. Border mode currently supported for this layer is 'valid'. You can also use AtrousConv1D as an alias of this layer. The input of this layer should be 3D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies an atrous convolution operator for filtering windows of 2-D inputs.
Applies an atrous convolution operator for filtering windows of 2-D inputs. A.k.a dilated convolution or convolution with holes. Bias will be included in this layer. Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. You can also use AtrousConv2D as an alias of this layer. The input of this layer should be 4D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension). e.g. input_shape=Shape(3, 128, 128) for 128x128 RGB pictures.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies average pooling operation for temporal data.
Applies average pooling operation for temporal data. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies average pooling operation for spatial data.
Applies average pooling operation for spatial data. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies average pooling operation for 3D data (spatial or spatio-temporal).
Applies average pooling operation for 3D data (spatial or spatio-temporal). Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
BERT A self attention Keras-style layer.
BERT A self attention Keras-style layer.
Input is a Table which consists of 4 tensors.
1. Token id tensor: shape [batch, seqLen] with the word token indices in the vocabulary
2. Token type id tensor: shape [batch, seqLen] with the token types in [0, 1].
0 means sentence A
and 1 means a sentence B
(see BERT paper for more details).
3. Position id tensor: shape [batch, seqLen] with positions in the sentence.
4. Attention_mask tensor: shape [batch, seqLen] with indices in [0, 1].
It's a mask to be used if the input sequence length is smaller than seqLen in
the current batch.
Output is a Table as well. 1. The states of BERT layer. 2. The pooled output which processes the hidden state of the last layer with regard to the first token of the sequence. This would be useful for segment-level tasks.
Batch normalization layer.
Batch normalization layer. Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. It is a feature-wise normalization, each feature map in the input will be normalized separately. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Bidirectional wrapper for RNNs.
Bidirectional wrapper for RNNs. Bidirectional currently requires RNNs to return the full sequence, i.e. returnSequences = true.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Example of creating a Bidirectional LSTM: Bidirectional(LSTM(12, returnSequences = true), mergeMode = "sum", inputShape = Shape(32, 32))
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Threshold the input.
Threshold the input. If an input element is smaller than the threshold value, it will be replaced by 0; otherwise, it will be replaced by 1.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
This layer has a bias with given size.
This layer has a bias with given size. The bias will be added element-wise to the input. If the element number of the bias matches the input, a simple element-wise addition will be done. Or the bias will be expanded to the same size of the input. The expand means repeat on unmatched singleton dimension (if some unmatched dimension isn't a singleton dimension, an error will be raised).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
This layer has a weight with given size.
This layer has a weight with given size. The weight will be multiplied element-wise to the input. If the element number of the weight matches the input, a simple element-wise multiplication will be done. Or the bias will be expanded to the same size of the input. The expand means repeat on unmatched singleton dimension (if some unmatched dimension isn't singleton dimension, an error will be raised).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Convolutional LSTM.
Convolutional LSTM. Note that currently only 'same' padding is supported. The convolution kernel for this layer is a square kernel with equal strides. The input of this layer should be 5D, i.e. (samples, time, channels, rows, cols), and 'CHANNEL_FIRST' (dimOrdering='th') is expected.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Convolutional LSTM for 3D input.
Convolutional LSTM for 3D input. Note that currently only 'same' padding is supported. The convolution kernel for this layer is a cubic kernel with equal strides for all dimensions. The input of this layer should be 6D, i.e. (samples, time, channels, dim1, dim2, dim3), and 'CHANNEL_FIRST' (dimOrdering='th') is expected.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies convolution operator for filtering neighborhoods of 1-D inputs.
Applies convolution operator for filtering neighborhoods of 1-D inputs. You can also use Conv1D as an alias of this layer. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies a 2D convolution over an input image composed of several input planes.
Applies a 2D convolution over an input image composed of several input planes. You can also use Conv2D as an alias of this layer. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension), e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies convolution operator for filtering windows of three-dimensional inputs.
Applies convolution operator for filtering windows of three-dimensional inputs. You can also use Conv3D as an alias of this layer. Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension), e.g. inputShape=Shape(3, 10, 128, 128) 10 frames of 128x128 RGB pictures.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Cropping layer for 1D input (e.g.
Cropping layer for 1D input (e.g. temporal sequence). The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Cropping layer for 2D input (e.g.
Cropping layer for 2D input (e.g. picture). The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Cropping layer for 3D data (e.g.
Cropping layer for 3D data (e.g. spatial or spatio-temporal). The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Transposed convolution operator for filtering windows of 2-D inputs.
Transposed convolution operator for filtering windows of 2-D inputs. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. You can also use Deconv2D as an alias of this layer. The input of this layer should be 4D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension). e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
A densely-connected NN layer.
A densely-connected NN layer. The most common input is 2D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each update during training time in order to prevent overfitting.
Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each update during training time in order to prevent overfitting.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Exponential Linear Unit.
Exponential Linear Unit. It follows: f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Turn non-negative integers (indices) into dense vectors of fixed size.
Turn non-negative integers (indices) into dense vectors of fixed size. The input of this layer should be 2D.
This layer can only be used as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies element-wise exp to the input.
Applies element-wise exp to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Flattens the input without affecting the batch size.
Flattens the input without affecting the batch size. For example, if inputShape = Shape(2, 3, 4), then outputShape will be Shape(24) with batch dimension unchanged.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Gated Recurrent Unit architecture.
Gated Recurrent Unit architecture. The input of this layer should be 3D, i.e. (batch, time steps, input dim).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Apply multiplicative 1-centered Gaussian noise.
Apply multiplicative 1-centered Gaussian noise. As it is a regularization layer, it is only active at training time.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Apply additive zero-centered Gaussian noise.
Apply additive zero-centered Gaussian noise. This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Takes {mean, log_variance} as input and samples from the Gaussian distribution
Takes {mean, log_variance} as input and samples from the Gaussian distribution
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now
Applies global average pooling operation for temporal data.
Applies global average pooling operation for temporal data. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies global average pooling operation for spatial data.
Applies global average pooling operation for spatial data. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies global average pooling operation for 3D data.
Applies global average pooling operation for 3D data. Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies global max pooling operation for temporal data.
Applies global max pooling operation for temporal data. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies global max pooling operation for spatial data.
Applies global max pooling operation for spatial data. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies global max pooling operation for 3D data.
Applies global max pooling operation for 3D data. Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies the hard shrinkage function element-wise to the input.
Applies the hard shrinkage function element-wise to the input.
⎧ x, if x > value f(x) = ⎨ x, if x < -value ⎩ 0, otherwise
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies the hard tanh function element-wise to the input.
Applies the hard tanh function element-wise to the input.
⎧ maxValue, if x > maxValue f(x) = ⎨ minValue, if x < minValue ⎩ x, otherwise
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Densely connected highway network.
Densely connected highway network. Highway layers are a natural extension of LSTMs to feedforward networks. The input of this layer should be 2D, i.e. (batch, input dim).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Identity just return the input to output.
Identity just return the input to output. It's useful in same parallel container to get an origin input.
Wrap a torch style layer to keras style layer.
Wrap a torch style layer to keras style layer. This layer can be built multiple times.
a keras compatible layer
Local Response Normalization between different feature maps.
Local Response Normalization between different feature maps.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Long Short Term Memory unit architecture.
Long Short Term Memory unit architecture. The input of this layer should be 3D, i.e. (batch, time steps, input dim).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Normalization layer used in Bert.
Normalization layer used in Bert. u = AutoGrad.mean(x, sizes.size - 1, true) t = x - u s = AutoGrad.mean(AutoGrad.square(x - u), sizes.size -1, true) y = (x - u) / AutoGrad.sqrt(s + e) y * weight + bias
Numeric type. Only support float/double now
Leaky version of a Rectified Linear Unit.
Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active: f(x) = alpha * x for x < 0, f(x) = x for x >= 0.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. Border mode currently supported for this layer is 'valid'. The input of this layer should be 3D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. The input of this layer should be 4D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies a log transformation to the input.
Applies a log transformation to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.

 Masks a sequence by using a mask value to skip timesteps.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies a max operation over dimension dim
Applies a max operation over dimension dim
Numeric type. Only support float/double now
Applies max pooling operation for temporal data.
Applies max pooling operation for temporal data. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now
Applies max pooling operation for spatial data.
Applies max pooling operation for spatial data. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies max pooling operation for 3D data (spatial or spatio-temporal).
Applies max pooling operation for 3D data (spatial or spatio-temporal). Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). Border mode currently supported for this layer is 'valid'. The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now
A dense maxout layer that takes the element-wise maximum of linear layers.
A dense maxout layer that takes the element-wise maximum of linear layers. This allows the layer to learn a convex, piecewise linear activation function over the inputs. The input of this layer should be 2D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Used to merge a list of inputs into a single output, following some merge mode.
Used to merge a list of inputs into a single output, following some merge mode. To merge layers, it must take at least two input layers.
When using this layer as the first layer in a model, you need to provide the argument inputShape for input layers (each as a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Multiply a single scalar factor to the input.
Multiply a single scalar factor to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Multiply the input by a (non-learnable) scalar constant.
Multiply the input by a (non-learnable) scalar constant.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Narrow the input with the number of dimensions not being reduced.
Narrow the input with the number of dimensions not being reduced. The batch dimension needs to be unchanged. For example, if input is: 1 2 3 4 5 6 Narrow(1, 1, 2) will give output 2 3 5 6 Narrow(1, 2, -1) will give output 3 6
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Computes the negative value of each element of the input.
Computes the negative value of each element of the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies parametric ReLU, where parameter varies the slope of the negative part.
Applies parametric ReLU, where parameter varies the slope of the negative part.
f(x) = max(0, x) + a * min(0, x)
Notice: Please don't use weight decay on this.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Permutes the dimensions of the input according to a given pattern.
Permutes the dimensions of the input according to a given pattern. Useful for connecting RNNs and convnets together.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Do not create a new instance of it or use it in a model.
Do not create a new instance of it or use it in a model. Please use its child classes, 'AveragePooling2D' and 'MaxPooling2D' instead.
Do not create a new instance of it or use it in a model.
Do not create a new instance of it or use it in a model. Please use its child classes, 'AveragePooling2D' and 'MaxPooling2D' instead.
Applies an element-wise power operation with scale and shift to the input.
Applies an element-wise power operation with scale and shift to the input.
f(x) = (shift + scale * x)power
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies the randomized leaky rectified linear unit element-wise to the input.
Applies the randomized leaky rectified linear unit element-wise to the input.
f(x) = max(0,x) + a * min(0, x) where a ~ U(l, u).
In the training mode, negative inputs are multiplied by a factor drawn from a uniform random distribution U(l, u). In the evaluation mode, a RReLU behaves like a LeakyReLU with a constant mean factor a = (l + u) / 2. If l == u, a RReLU essentially becomes a LeakyReLU. Regardless of operating in in-place mode a RReLU will internally allocate an input-sized noise tensor to store random factors for negative inputs. For reference, see [Empirical Evaluation of Rectified Activations in Convolutional Network](http://arxiv.org/abs/1505.00853).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Repeats the input n times.
Repeats the input n times. The input of this layer should be 2D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Reshapes an output to a certain shape.
Reshapes an output to a certain shape. The batch dimension needs to be unchanged. Supports shape inference by allowing one -1 in the target shape. For example, if inputShape = Shape(2, 3, 4), targetShape = Array(3, -1), then outputShape will be Shape(3, 8).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Resize the input image with bilinear interpolation.
Resize the input image with bilinear interpolation. The input image must be a float tensor with NHWC or NCHW layout.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now
S-shaped Rectified Linear Unit.
S-shaped Rectified Linear Unit. It follows: f(x) = tr + ar(x - tr) for x >= tr, f(x) = x for tr > x > tl, f(x) = tl + al(x - tl) for x <= tl.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Scale is the combination of CMul and CAdd.
Scale is the combination of CMul and CAdd.
Computes the element-wise product of the input and weight, with the shape of the weight "expand" to match the shape of the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Select an index of the input in the given dim and return the subset part.
Select an index of the input in the given dim and return the subset part. The batch dimension needs to be unchanged. For example, if input is: 1 2 3 4 5 6 Select(1, 1) will give output [2 5] Select(1, -1) will give output [3 6]
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Creates a module that takes a table as input and outputs the element at index index
Creates a module that takes a table as input and outputs the element at index index
When you use this layer as the first layer of a model, you need to provide the argument inputShape.
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies separable convolution operator for 2D inputs.
Applies separable convolution operator for 2D inputs. Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The depthMultiplier argument controls how many output channels are generated per input channel in the depthwise step. You can also use SeparableConv2D as an alias of this layer. The input of this layer should be 4D.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension). e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies a 2D convolution over an input image composed of several input planes.
Applies a 2D convolution over an input image composed of several input planes. You can also use ShareConv2D as an alias of this layer. Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension), e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
A fully-connected recurrent neural network cell.
A fully-connected recurrent neural network cell. The output is to be fed back to input. The input of this layer should be 3D, i.e. (batch, time steps, input dim).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies the SoftMax function to an n-dimensional input Tensor, rescaling them so that the elements of the n-dimensional output Tensor lie in the range (0, 1) and sum to 1.
Applies the SoftMax function to an n-dimensional input Tensor, rescaling them so that the elements of the n-dimensional output Tensor lie in the range (0, 1) and sum to 1. Softmax is defined as: f_i(x) = exp(x_i - shift) / sum_j exp(x_j - shift) where shift = max_i(x_i). Currently only support apply softmax normalization to the last dim.
Applies the soft shrinkage function element-wise to the input.
Applies the soft shrinkage function element-wise to the input.
⎧ x - value, if x > value f(x) = ⎨ x + value, if x < -value ⎩ 0, otherwise
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
SparseDense is the sparse version of layer Dense.
SparseDense is the sparse version of layer Dense. SparseDense has two different from Dense: firstly, SparseDense's input Tensor is a SparseTensor. Secondly, SparseDense doesn't backward gradient to next layer in the backpropagation by default, as the gradInput of SparseDense is useless and very big in most cases.
But, considering model like Wide&Deep, we provide backwardStart and backwardLength to backward part of the gradient to next layer.
The most common input is 2D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
SparseEmbedding is the sparse version of layer Embedding.
SparseEmbedding is the sparse version of layer Embedding.
The input of SparseEmbedding should be a 2D SparseTensor or two 2D sparseTensors. If the input is a SparseTensor, the values are positive integer ids, values in each row of this SparseTensor will be turned into a dense vector. If the input is two SparseTensors, the first tensor should be the integer ids, just like the SparseTensor input. And the second tensor is the corresponding weights of the integer ids.
This layer can only be used as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Spatial 1D version of Dropout.
Spatial 1D version of Dropout. This version performs the same function as Dropout, however it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Spatial 2D version of Dropout.
Spatial 2D version of Dropout. This version performs the same function as Dropout, however it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Spatial 3D version of Dropout.
Spatial 3D version of Dropout. This version performs the same function as Dropout, however it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead. The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Creates a module that takes a Tensor as input and
outputs tables, splitting the Tensor along
the specified dimension dimension
.
Creates a module that takes a Tensor as input and
outputs tables, splitting the Tensor along
the specified dimension dimension
. Please note the dimension starts from 0.
Numeric type. Only support float/double now
Applies an element-wise square root operation to the input.
Applies an element-wise square root operation to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Applies an element-wise square operation to the input.
Applies an element-wise square operation to the input.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Delete the singleton dimension(s).
Delete the singleton dimension(s). The batch dimension needs to be unchanged. For example, if input has size (2, 1, 3, 4, 1): Squeeze(dim = 1) will give output size (2, 3, 4, 1) Squeeze(dims = null) will give output size (2, 3, 4)
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
Threshold input Tensor.
Threshold input Tensor. If values in the Tensor smaller than th, then replace it with v.
Thresholded Rectified Linear Unit.
Thresholded Rectified Linear Unit. It follows: f(x) = x for x > theta, f(x) = 0 otherwise.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
TimeDistributed wrapper.
TimeDistributed wrapper. Apply a layer to every temporal slice of an input. The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.
When using this layer as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
If you apply TimeDistributed to a Dense layer, you can use: TimeDistributed(Dense(8), inputShape = Shape(10, 12))
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
UpSampling layer for 1D inputs.
UpSampling layer for 1D inputs. Repeats each temporal step 'length' times along the time axis. The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
UpSampling layer for 2D inputs.
UpSampling layer for 2D inputs. Repeats the rows and columns of the data by size(0) and size(1) respectively. The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
UpSampling layer for 3D inputs.
UpSampling layer for 3D inputs. Repeats the 1st, 2nd and 3rd dimensions of the data by size(0), size(1) and size(2) respectively. Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions.
The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. The local regions extend spatially, in separate channels (i.e., they have shape 1 x size x size).
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Remark: This layer is from Torch and wrapped in Keras style.
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Embedding layer that directly loads pre-trained word vectors as weights.
Embedding layer that directly loads pre-trained word vectors as weights. Turn non-negative integers (indices) into dense vectors of fixed size. Currently only GloVe embedding is supported. The input of this layer should be 2D.
This layer can only be used as the first layer in a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Zero-padding layer for 1D input (e.g.
Zero-padding layer for 1D input (e.g. temporal sequence). The input of this layer should be 3D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Zero-padding layer for 2D input (e.g.
Zero-padding layer for 2D input (e.g. picture). The input of this layer should be 4D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Zero-padding layer for 3D data (spatial or spatio-temporal).
Zero-padding layer for 3D data (spatial or spatio-temporal). The input of this layer should be 5D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
The numeric type of parameter(e.g. weight, bias). Only support float/double now.
Used to instantiate an input node.
Used as an entry point into a model.


#include "blockchain.h"
#include "ckb_syscalls.h"
A simple UDT script using 128 bit unsigned integer range
This UDT has 2 unlocking modes:
Notice one caveat of this UDT script is that only one UDT can be issued for each unique lock script. A more sophisticated UDT script might include other arguments(such as the hash of the first input) as a unique identifier, however for the sake of simplicity, we are happy with this limitation.
First, let’s include header files used to interact with CKB.
#include "blockchain.h"
#include "ckb_syscalls.h"
We are limiting the script size loaded to be 32KB at most. This should be more than enough. We are also using blake2b with 256-bit hash here, which is the same as CKB.
#define BLAKE2B_BLOCK_SIZE 32
#define SCRIPT_SIZE 32768
Common error codes that might be returned by the script.
#define ERROR_ARGUMENTS_LEN -1
#define ERROR_ENCODING -2
#define ERROR_SYSCALL -3
#define ERROR_SCRIPT_TOO_LONG -21
#define ERROR_OVERFLOWING -51
#define ERROR_AMOUNT -52
We will leverage gcc’s 128-bit integer extension here for number crunching.
typedef unsigned __int128 uint128_t;
int main() {
First, let’s load current running script, so we can extract owner lock script hash from script args.
unsigned char script[SCRIPT_SIZE];
uint64_t len = SCRIPT_SIZE;
int ret = ckb_load_script(script, &len, 0);
if (ret != CKB_SUCCESS) {
return ERROR_SYSCALL;
}
if (len > SCRIPT_SIZE) {
return ERROR_SCRIPT_TOO_LONG;
}
mol_seg_t script_seg;
script_seg.ptr = (uint8_t *)script;
script_seg.size = len;
if (MolReader_Script_verify(&script_seg, false) != MOL_OK) {
return ERROR_ENCODING;
}
mol_seg_t args_seg = MolReader_Script_get_args(&script_seg);
mol_seg_t args_bytes_seg = MolReader_Bytes_raw_bytes(&args_seg);
if (args_bytes_seg.size != BLAKE2B_BLOCK_SIZE) {
return ERROR_ARGUMENTS_LEN;
}
With owner lock script extracted, we will look through each input in the current transaction to see if any unlocked cell uses owner lock.
int owner_mode = 0;
size_t i = 0;
while (1) {
uint8_t buffer[BLAKE2B_BLOCK_SIZE];
uint64_t len = BLAKE2B_BLOCK_SIZE;
There are 2 points worth mentioning here:
CKB_CELL_FIELD_LOCK_HASH
is used here to directly load the
lock script hash, so we don’t have to manually calculate the hash again
here. ret = ckb_checked_load_cell_by_field(buffer, &len, 0, i, CKB_SOURCE_INPUT,
CKB_CELL_FIELD_LOCK_HASH);
if (ret == CKB_INDEX_OUT_OF_BOUND) {
break;
}
if (ret != CKB_SUCCESS) {
return ret;
}
if (len != BLAKE2B_BLOCK_SIZE) {
return ERROR_ENCODING;
}
if (memcmp(buffer, args_bytes_seg.ptr, BLAKE2B_BLOCK_SIZE) == 0) {
owner_mode = 1;
break;
}
i += 1;
}
When owner mode is triggered, we won’t perform any checks here, the owner is free to make any changes here, including token issurance, minting, etc.
if (owner_mode) {
return CKB_SUCCESS;
}
When the owner mode is not enabled, however, we will then need to ensure the sum of all input tokens is not smaller than the sum of all output tokens. First, let’s loop through all input cells containing current UDTs, and gather the sum of all input tokens.
uint128_t input_amount = 0;
i = 0;
while (1) {
uint128_t current_amount = 0;
len = 16;
The implementation here does not require that the transaction only
contains UDT cells for the current UDT type. It’s perfectly fine to mix
the cells for multiple different types of UDT together in one
transaction. But that also means we need a way to tell one UDT type from
another UDT type. The trick is in the CKB_SOURCE_GROUP_INPUT
value used
here. When using it as the source part of the syscall, the syscall would
only iterate through cells with the same script as the current running
script. Since different UDT types will naturally have different
script(the args part will be different), we can be sure here that this
loop would only iterate through UDTs that are of the same type as the one
identified by the current running script.
In the case that multiple UDT types are included in the same transaction, this simple UDT script will be run multiple times to validate the transaction, each time with a different script containing different script args, representing different UDT types.
A different trick used here, is that our current implementation assumes
that the amount of UDT is stored as unsigned 128-bit little endian
integer in the first 16 bytes of cell data. Since RISC-V also uses little
endian format, we can just read the first 16 bytes of cell data into
current_amount
, which is just an unsigned 128-bit integer in C. The
memory layout of a C program will ensure that the value is set correctly.
ret = ckb_load_cell_data((uint8_t *)¤t_amount, &len, 0, i,
CKB_SOURCE_GROUP_INPUT);
When CKB_INDEX_OUT_OF_BOUND
is reached, we know we have iterated
through all cells of current type.
if (ret == CKB_INDEX_OUT_OF_BOUND) {
break;
}
if (ret != CKB_SUCCESS) {
return ret;
}
if (len != 16) {
return ERROR_ENCODING;
}
input_amount += current_amount;
Like any serious smart contract out there, we will need to check for overflows.
if (input_amount < current_amount) {
return ERROR_OVERFLOWING;
}
i += 1;
}
With the sum of all input UDT tokens gathered, let’s now iterate through output cells to grab the sum of all output UDT tokens.
uint128_t output_amount = 0;
i = 0;
while (1) {
uint128_t current_amount = 0;
len = 16;
Similar to the above code piece, we are also looping through output cells
with the same script as current running script here by using
CKB_SOURCE_GROUP_OUTPUT
.
ret = ckb_load_cell_data((uint8_t *)¤t_amount, &len, 0, i,
CKB_SOURCE_GROUP_OUTPUT);
if (ret == CKB_INDEX_OUT_OF_BOUND) {
break;
}
if (ret != CKB_SUCCESS) {
return ret;
}
if (len != 16) {
return ERROR_ENCODING;
}
output_amount += current_amount;
Like any serious smart contract out there, we will need to check for overflows.
if (output_amount < current_amount) {
return ERROR_OVERFLOWING;
}
i += 1;
}
When both value are gathered, we can perform the final check here to prevent non-authorized token issurance.
if (input_amount < output_amount) {
return ERROR_AMOUNT;
}
return CKB_SUCCESS;
}


--- layout: post status: publish published: true title: Viasfora v1.1 Published author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 1385 wordpress_url: http://winterdom.com/?p=1385 date: '2013-11-25 01:20:34 +0000' date_gmt: '2013-11-25 01:20:34 +0000' categories: - Visual Studio tags: [] comments: - id: 36952 author: suresh2 author_email<EMAIL_ADDRESS>author_url: '' date: '2013-11-25 10:34:33 +0000' date_gmt: '2013-11-25 10:34:33 +0000' content: Really useful and great extension. I can read my code much better now. Thanks for adding more colours to VS. :-) The link Viasfora is broken in the article. Please fix it. Thanks. - id: 37000 author: John Graybosch author_email<EMAIL_ADDRESS>author_url: http://johngraybosch.com date: '2013-11-25 14:26:08 +0000' date_gmt: '2013-11-25 14:26:08 +0000' content: Does it support VB.NET? - id: 37024 author: Tomas Restrepo author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ date: '2013-11-25 16:18:11 +0000' date_gmt: '2013-11-25 16:18:11 +0000' content: Viasfora does support VB for some features, but rainbow parentheses isn't supported on this initial version. I will try to add support for it in vNext (it requires writing a separate parser for VB, but it shouldn't be too hard). ---
Tonight I pushed a new version of my Viasfora extension for Visual Studio. In this version, I fixed some features that were not working on Visual Studio 2013 due to the introduction of a new HTML editor, particularly highlighting closing HTML tags.
What's exiting to me, however, is the new feature: Rainbow Parentheses:
[caption id="attachment_1388" align="alignnone" width="501"] Rainbow Parentheses[/caption]
This is a Visual Studio version inspired by one of my favorite Vim plugins. Features:
Comments, bug reports and feature requests are always welcome. Enjoy!


From the TLC Trip Record data, this visualisation shows a day of taxi trips to and from La Grande Airport.
Technologies: BigQuery, Python, Node.js, D3.js
View ProjectThe visualisation shows the relationships within the teams of the methodology division and among the other disions of the Office for National Statistics.
Technology: D3.js
View ProjectLorem ipsum dolor sit amet, consectetur adipisicing elit. Explicabo, quidem, consectetur, officia rem officiis illum aliquam perspiciatis aspernatur quod modi hic nemo qui soluta aut eius fugit quam in suscipit?
This is one of the visualisations I developed for the keynote of a tech conference in Italy. The visualisations were aimed at showing the increase of diversity within speakers and attendees, in terms of internationality and gender. As selected showcase I propose here the visualisation that shows the rising number of women attendees in the 3 edition of the conference, as well as the presence of 4 women speakers in the 2015 edition. To see the visualisation press on the two headers.
Technology: D3.js
View Project


Ocean [ax72], ocean, in Crystal Sea
Last updated Turn #124
Ocean [ax72], ocean, in Crystal Sea
------------------------------------------------------------------------
4: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
4: from Ocean [aw72], owner:
4: Dorothy [v1b], Pen, with 40 soldiers, 94 sailors, accompanied by:
4: Cindy [z1c], Pen, with 23 peasants, one worker, 62 soldiers
5: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
5: departed for Ocean [ax71].
30: The fog has cleared.
Routes leaving Ocean:
North, to Ocean [aw72], 6 days
East, to Ocean [ax73], 6 days
South, to Ocean [ay72], 6 days
West, to Ocean [ax71], 6 days
Inner locations:
Island [ab7u], island, 1 day, owner:
Pirate [cy6u], pirates, number: 6


{% if output %}
The results are as follows
{{ output|safe }}
{% endif %} {% if not output %}
The simulation is currently still being performed.
{% endif %}


Production Details
Casting the TV pilot, "Coney Island." Project description: "Pilot for a tv drama series based on characters from Phantom of the Opera, centering around a Freak Show run by Meg & Th...more
This casting/job notice is still under review by Backstage's editors. However, you can apply right away! Scams and inappropriate content will be promptly removed.
TV & Video: Scripted TV & Video
Production: 'Coney Island' (See all 3 roles)
Production Details
Casting the TV pilot, "Coney Island." Project description: "Pilot for a tv drama series based on characters from Phantom of the Opera, centering around a Freak Show run by Meg & Th...more
Role Description
Actors: (Supporting) all characters based on Phantom of the Opera characters, English, French, Cockney and Persian accents a plus.


| Package | Description |
|---|---|
| com.destroystokyo.paper |
| Modifier and Type | Field and Description |
|---|---|
static MaterialSetTag |
MaterialTags.ARROWS |
static MaterialSetTag |
MaterialTags.AXES
Covers the variants of axes.
|
static MaterialSetTag |
MaterialTags.BEDS
Covers all colors of beds.
|
static MaterialSetTag |
MaterialTags.BOOTS
Covers the variants of boots.
|
static MaterialSetTag |
MaterialTags.BOWS
Covers the variants of bows.
|
static MaterialSetTag |
MaterialTags.BUCKETS
Covers all bucket items.
|
static MaterialSetTag |
MaterialTags.CHEST_EQUIPPABLE
Covers the variants of items that can be equipped in the chest slot.
|
static MaterialSetTag |
MaterialTags.CHESTPLATES
Covers the variants of chestplate.
|
static MaterialSetTag |
MaterialTags.COALS
Covers coal and charcoal.
|
static MaterialSetTag |
MaterialTags.COBBLESTONE_WALLS
Covers both cobblestone wall variants.
|
static MaterialSetTag |
MaterialTags.COBBLESTONES
Covers both cobblestone and mossy Cobblestone.
|
static MaterialSetTag |
MaterialTags.COLORABLE
Covers materials that can be colored, such as wool, shulker boxes, stained glass etc.
|
static MaterialSetTag |
MaterialTags.CONCRETE_POWDER
Covers all colors of concrete powder.
|
static MaterialSetTag |
MaterialTags.CONCRETES
Covers all colors of concrete.
|
static MaterialSetTag |
MaterialTags.COOKED_FISH
Covers the two types of cooked fish.
|
static MaterialSetTag |
MaterialTags.CORAL
Covers the variants of coral.
|
static MaterialSetTag |
MaterialTags.CORAL_BLOCKS
Covers the variants of coral blocks.
|
static MaterialSetTag |
MaterialTags.CORAL_FANS
Covers the variants of coral fans.
|
static MaterialSetTag |
MaterialTags.DOORS
Covers all variants of doors.
|
static MaterialSetTag |
MaterialTags.DYES
Covers all dyes.
|
static MaterialSetTag |
MaterialTags.ENCHANTABLE
Covers all items that can be enchanted from the enchantment table or anvil.
|
static MaterialSetTag |
MaterialTags.FENCE_GATES
Covers all variants of gates.
|
static MaterialSetTag |
MaterialTags.FENCES
Covers all variants of fences.
|
static MaterialSetTag |
MaterialTags.FISH_BUCKETS
Covers all variants of fish buckets.
|
static MaterialSetTag |
MaterialTags.GLASS
Covers the non-colored glass and 16 stained glass (not panes).
|
static MaterialSetTag |
MaterialTags.GLASS_PANES
Covers the non-colored glass panes and stained glass panes (panes only).
|
static MaterialSetTag |
MaterialTags.GLAZED_TERRACOTTA
Covers all glazed terracotta blocks.
|
static MaterialSetTag |
MaterialTags.GOLDEN_APPLES
Covers both golden apples.
|
static MaterialSetTag |
MaterialTags.HEAD_EQUIPPABLE
Covers the variants of items that can be equipped in the helmet slot.
|
static MaterialSetTag |
MaterialTags.HELMETS
Covers the variants of helmets.
|
static MaterialSetTag |
MaterialTags.HOES
Covers the variants of hoes.
|
static MaterialSetTag |
MaterialTags.HORSE_ARMORS
Covers the variants of horse armor.
|
static MaterialSetTag |
MaterialTags.INFESTED_BLOCKS
Covers the variants of infested blocks.
|
static MaterialSetTag |
MaterialTags.LANTERNS
Covers the variants of lanterns.
|
static MaterialSetTag |
MaterialTags.LEGGINGS
Covers the variants of leggings.
|
static MaterialSetTag |
MaterialTags.MUSHROOM_BLOCKS
Covers the variants of mushroom blocks.
|
static MaterialSetTag |
MaterialTags.MUSHROOMS
Covers all mushrooms.
|
static MaterialSetTag |
MaterialTags.MUSIC_DISCS
Covers all music disc items.
|
static MaterialSetTag |
MaterialTags.ORES
Covers all ores.
|
static MaterialSetTag |
MaterialTags.PICKAXES
Covers the variants of pickaxes.
|
static MaterialSetTag |
MaterialTags.PISTONS
Covers all piston typed items and blocks including the piston head and moving piston.
|
static MaterialSetTag |
MaterialTags.POTATOES
Covers all potato items.
|
static MaterialSetTag |
MaterialTags.PRESSURE_PLATES
Covers all wooden pressure plates and the weighted pressure plates and the stone pressure plate.
|
static MaterialSetTag |
MaterialTags.PRISMARINE
Covers the variants of prismarine blocks.
|
static MaterialSetTag |
MaterialTags.PRISMARINE_SLABS
Covers the variants of prismarine slabs.
|
static MaterialSetTag |
MaterialTags.PRISMARINE_STAIRS
Covers the variants of prismarine stairs.
|
static MaterialSetTag |
MaterialTags.PUMPKINS
Covers the variants of pumpkins.
|
static MaterialSetTag |
MaterialTags.PURPUR
Covers the variants of purpur.
|
static MaterialSetTag |
MaterialTags.QUARTZ_BLOCKS
Covers the variants of quartz blocks.
|
static MaterialSetTag |
MaterialTags.RAILS
Covers the variants of rails.
|
static MaterialSetTag |
MaterialTags.RAW_FISH
Covers all uncooked fish items.
|
static MaterialSetTag |
MaterialTags.RED_SANDSTONES
Covers the variants of red sandstone blocks.
|
static MaterialSetTag |
MaterialTags.REDSTONE_TORCH
Covers the variants of a redstone torch.
|
static MaterialSetTag |
MaterialTags.SANDSTONES
Covers the variants of sandstone blocks.
|
static MaterialSetTag |
MaterialTags.SHOVELS
Covers the variants of shovels.
|
static MaterialSetTag |
MaterialTags.SHULKER_BOXES
Covers the non-colored and colored shulker boxes.
|
static MaterialSetTag |
MaterialTags.SIGNS
Covers the variants of signs.
|
static MaterialSetTag |
MaterialTags.SKULLS
Covers zombie, creeper, skeleton, dragon, and player heads.
|
static MaterialSetTag |
MaterialTags.SOUL_TORCH
Covers the variants of a soul torch.
|
static MaterialSetTag |
MaterialTags.SPAWN_EGGS
Covers all spawn egg items.
|
static MaterialSetTag |
MaterialTags.SPONGES
Covers sponge and wet sponge.
|
static MaterialSetTag |
MaterialTags.STAINED_GLASS
Covers all colors of stained glass.
|
static MaterialSetTag |
MaterialTags.STAINED_GLASS_PANES
Covers all colors of stained glass panes.
|
static MaterialSetTag |
MaterialTags.STAINED_TERRACOTTA
Covers the colors of stained terracotta.
|
static MaterialSetTag |
MaterialTags.SWORDS
Covers the variants of swords.
|
static MaterialSetTag |
MaterialTags.TERRACOTTA
Covers terracotta along with the stained variants.
|
static MaterialSetTag |
MaterialTags.THROWABLE_PROJECTILES
Covers the variants of player-throwable projectiles (not requiring a bow or any other "assistance").
|
static MaterialSetTag |
MaterialTags.TORCH
Covers the variants of a regular torch.
|
static MaterialSetTag |
MaterialTags.TORCHES
Covers the variants of torches.
|
static MaterialSetTag |
MaterialTags.TRAPDOORS
Covers all variants of trapdoors.
|
static MaterialSetTag |
MaterialTags.WOODEN_DOORS
Covers all wood variants of doors.
|
static MaterialSetTag |
MaterialTags.WOODEN_FENCES
Covers all wood variants of fences.
|
static MaterialSetTag |
MaterialTags.WOODEN_GATES
Covers the wood variants of gates.
|
static MaterialSetTag |
MaterialTags.WOODEN_TRAPDOORS
Covers all wood variants of trapdoors.
|
Copyright © 2021. All rights reserved.


SERVERS
Roleplay Server
Silent Gaming Network Main Server
Server is offline.
Player's online:
Join Server
Players


I've never really been tied to any specific nicknames, but I would love for you to think of me as your new favorite multi-talented enthusiast. I find it hazardous to label myself as just one skill — web developer, C# developer, photographer, producer — simply because I feel like doing so would limit me to one medium. If I had to label myself as anything it would have to be a creative. Creating has always been my passion. Whether I was building pillow forts as a child or I was developing my first website at the age of seventeen, I've always found some way to inject my creative spirit into everything I do.
So I've talked the talk now it's probably best to show you that I can also walk the walk.
— JAVA
Utilizing JavaFX to create graphical user interfaces using the controls offered within Scene Builder.
See What I Did →Utilizing Arrays and Array Lists to store grouped data; Creating methods to sort and search arrays.
See What I Did →Extending super classes to derived classes in order to simplflify the object creation process; Using related classes within one another.
See What I Did →Handling exceptions thrown within programs; Creating custom exceptions to be thrown when invalid data is entered.
See What I Did →Implementing interfaces to utilize common features across all related classes.
See What I Did →Overriding inherited methods to peform different processee; Overloading method to provide a different action for different data types.
See What I Did →Declaring static class members to be used without the initialization of an object.
See What I Did →Peforming certain actions depending on a condition; Repeating block(s) of code that must run multiple times.
See What I Did →Utilizing constructors and instance members to define an object.
See What I Did →


{% include 'header.html' %}
Ben from GeoStrategies will be calling you to finalize your registration.


Done
Options
Refresh
Change Units
Set Location
hide sidebar
show sidebar
Droplets
Getting weather information...
back
Remove text
Unit Systems
Metric
Imperial
Cancel


Is
Your
Car
Fat?
Is Your Car Fat?
Is Your Car Fat?
Is Your Car Fat?
<EMAIL_ADDRESS>LinkedIn
Github
Is Your Car Fat?
Website Design by Gus Beringer


WINGS OF DESIRE A film review by Lon Ponschock Copyright 1995 Lon Ponschock
Directed by Wim Wenders Written by Wim Wenders with Peter Handke Starring Bruno Ganz, Solveig Dommartin, Otto Sander, Curt Bois, and Peter Falk 130 mins.
Here is the beginning of the story of Cassiel and Damiel: Guardian Angels. This film from 1988 is, for me, the first major work by Wim Wenders. His previous collaboration with Peter Handke on Handke's play called THE ANXIETY OF THE GOALIE AT THE PENALTY KICK goes back to a period around 1973. This play had become a modern classic in Germany. Okay, in Germany it's a classic. But for me the film treatment was nothing more than 'another bad European film shot on a budget and meant to look like *art*.'
But *here* in Wenders' original 'voice' both in language and film technique, *here* is a wonderful little film. To do so much with city streets, suggestions of costuming, and poetic dialogue is the art of the master or the genius, who can say which for sure? I've seen Wenders' work when it was really BAD too. An item called NOTEBOOK ON CITIES AND CLOTHES is still on the current rack at one of the vid shops around town.
The camera turns in WINGS do not have the flourish of the more recent companion piece which is called FAR AWAY SO CLOSE, but here the director can turn a simple building and it's shape into a place of magic and wonder. Observe the library sequences in WINGS to see what I mean.
The subject is angels--Guardian Angels--a subject which has come into vogue over the past two years in America. There is something very touching about this. It tells us that in our heart of hearts we yearn to not be alone. It is a belief that we want to cherish even though this is a simple work of fiction. Because the mind has conceived of it, it has become it's own reality. The spirits which the mind conjures become beliefs or, at the very least, wishes.
So WINGS OF DESIRE like all the angel shows before it, plays to these feelings.
And it works.
With a simple detail like a ponytail tied with a string, Wenders tells us here are the angels ... they see each other when no one else can and we see them too.
And there is this: a man is shown in despair. An angel embraces him. Then his inner voice gives him the strength to go on with his life. The embrace of the angel ends. The man sits up and continues on his journey.
So much with so little.
WINGS OF DESIRE is categorized as a comedy, like it was some sort of HEAVEN KNOWS MR. ALLISON or HEAVEN CAN WAIT and other such fluff. There are moments of humor in WINGS to be sure. But the film is so much more than that. So when you see it displayed do not be put off as I was *for months* because of a judgement that didn't relate to the work itself.
Get WINGS OF DESIRE and FAR AWAY SO CLOSE together if at all possible. WINGS OF DESIRE is only available at WorldWide Video on College Avenue in the Marketplace. FAR AWAY is probably in wider circulation.
I'll conclude this rave-up with a small quotation from the film. Wenders' work is poetic and he speaks to our hearts:
Damiel: "I want to conquer a history for myself I want to turn what I have learned from my timeless downward-watching into sustaining a hasty glance, a short shout, an acrid smell. I've been outside long enough, Absent long enough, Out of the world long enough. I'll enter the history of the world, if only to hold on apple in my hand."
-- lon "I did not know the cure for the disease of images, but I believed in the healing power of words and stories." - Until the End of the World
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


stdlib v22.1.4 shell_default
The functions in this module are called when no module name is specified in a shell command.
Consider the following shell dialog:
1> lists:reverse("abc").
"cba"
2> c(foo).
{ok, foo}
In command one, module lists
is called. In command two, no module name is specified. The shell searches module user_default
followed by module shell_default
for function c/1
.
shell_default
is intended for "system wide" customizations to the shell. user_default
is intended for "local" or individual user customizations.


NASA is an International Organization
An interface can declare properties, methods and events only.
An interface can declare properties, methods and events only.
An abstract class is always used as a base class. It provides some abstract/virtual members that the inheriting entities must implement, as well as a partial implementation for a functionality.
Worked on agile environment to build a .NET 4.5 web-based applications using C#.
Windows WorkFlow, Web Api, Entity Framework, Single Page Application, Knockout Js, using our online polciy sales application.
This is the new paragraph
This is the new paragraph
This is the new paragraph
This is the new paragraph
This is the new paragraph
This is the new paragraph
This is the new paragraph
This is the new paragraph


Are you filling your fridge with fresh food? Add each item here, together with the expiry date, or some estimate of the number of days it will stay fresh. MyFridge will in turn help you to avoid throwing away your food.
These are, based on what you told us, the contents of your fridge, sorted by expiry date. Please prepare something delicious with the topmost items, or you have to throw away stuff.
| Name | Days left | Expiry date | |
|---|---|---|---|
| {{ item.name }} | {{ get_days_left(item) }} days left | {{ item.expiry_date|date:"dd/MM/yyyy" }} |
MyFridge will help you to avoid throwing away your food. Want to try? Create an account and have fun!
Creating your fresh new account...


{% extends "layout.html" %} {% block content %}
This is a website that allows you to control various functions of your discord bot.


UserTiming.js Test Suite
Added window.performance.now() support?
Added PerformanceTimeline support?
Added UserTiming support?


{% extends "layout.html" %}
{% block title %}Download Worlds{% endblock %}
{% block body %}
STOP, READ THIS FIRST!
Terms of Use
These world files are for current MRT members ONLY. Do NOT share the download links, password, or world files to guests, banned players, or non-members.
You are free to use these worlds for any personal projects on a single-player world.
If you would like to host these worlds on your own multiplayer server, please ask for permission by contacting us at<EMAIL_ADDRESS>DO NOT STEAL any structures, projects, or designs from these worlds and DO NOT claim them as your own. Give credit to the original builder where it is due.
If you have read and agree to the above terms, then continue to the World Downloads.
{% endblock %}


Price Range
est. Total Cost for Trip
est. Stay Length
User Rating
Med Voyage Rating
Patient Responsiveness Dimensions
Government Ranking
Location
est. Wait Time
Specialties of provider
Medanta Hospital
Apollo Hospital Group
$27k - $32k
$30k - $36k
From $30k
From $32k
13 days
16 days
23
14
Gurgaon, India
Multiple Locations, India
2 Weeks
3 Weeks
Heart, Kidney
Heart, Orthopedics, Gastroenterology, Cancer, Liver, Bariatric


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 2
Elevate the humble trout with a ground almond crust. A great dish for cooking in the wild.
Dry the fish fillets with kitchen paper and set aside.
Take two bowls, in one add the milk, in the other add the seasoned flour and ground almonds.
Dip this fish fillets in the milk and then press into the almond flour mixture until completely coated.
Heat a frying pan and add the olive oil and half the butter. Place the fish into the hot pan skin side up and fry for 2 minutes until the surface is golden-brown and crisp. Turn the fish over and cook on the skin side for 5 minutes, or until cooked through. Put the fish on a warm plate and set aside.
Add the remaining butter to the frying pan, allow to bubble and then add the flaked almonds and cook for a few minutes until toasted. Add the parsley, capers, lemon zest and juice. Warm for about 30 seconds.
Place the fish fillets on a serving plate and sprinkle over the flaked almond mixture. Serve with new potatoes and green beans.


|
SIGN IN
|
|
FORGOT PASSWORD
|
|
DON'T HAVE AN ACCOUNT? SIGN UP
|
|
SIGN IN
|
|
FORGOT PASSWORD
|
|
DON'T HAVE AN ACCOUNT?  SIGN In
|


TBA adds cedar-clad extension to white farmhouse in Quebec
by James BrillonMontreal studio Thomas Balaban Architect has turned a gabled farmhouse in Quebec's Eastern Townships completely white, and then contrasted it with an extension covered in weathered cedar cladding.
The Knowlton Residence is located in rural Canada, a few hours outside of Montreal, in a popular ski destination. The clients enlisted the architects to turn their existing vacation into a residence where they could live permanently.
TBA added a two-storey volume was added to the site to site atop the footprint of a previous expansion. "Going up instead of spreading out allowed for more space and better views without the need to excavate across the hilltop," said the studio in a project description.
The barns that are abundant in the area influenced TBA's choices for the exterior treatment of both structures. The firm subverted the selection, however, updating the existing farmhouse in contemporary materials, and rendering it white, and then using more traditional materials for the extension.
"A new sleek corrugated metal roof and painted white brick update the existing structure, while the new extension is clad in custom cedar boarding, charred and silvered to replicate weathered wood," said TBA.
"However, both materials speak to the numerous old barns and newer agricultural sheds found throughout the Eastern Townships."
The main entrance to the 300-square-metre home leads into the original gabled structure, which contains the family's open-plan kitchen and dining room. This space enjoys higher ceilings because the of the roof's pitch, which is exposed inside.
This area connects to the sitting room located in the extension. The two volumes are linked by sliding glass doors that open from either room onto a terrace. "On the lower level, a band of windows cuts across both structures – a void that ties together the contrasting forms," said the architects.
"It wraps around the social spaces, giving out onto the majestic views of Brome Lake Valley and Mont Sutton."
A flight of wooden stairs flanked by a solid white guardrail leads upstairs, to the home's three bedrooms, each with an en-suite bathroom. At the top of the stairs, a media room separates the two smaller bedrooms, while the master suite is set slightly away from the others.
Inside, TBA picked minimal finishes throughout the home. "A simple material palette, and minimal detailing connect the interior spaces, subtlety highlighting geometric variations and moments where spatial intimacy meets openness," it said.
Pale white floors run throughout, complementing the owner's Scandinavian-inspired wooden furniture.
Other residences located throughout the Eastern Townships region include a property featuring of "industrial and earthy" materials by Tux Creative and architect Guillaume Kukucka, and a pair of blackened wooden cabins that form a minimalist chalet by Montreal firm naturehumaine.
Photography is by Adrien Williams.
Project credits:
Project Team: Thomas Balaban, Julia Manaças, Jennifer Thorogood, Mikaèle Fol
Contractor: Construction Laplume
Kitchen: Pure Cuisine


Hot Module Replacement
Hot Module Replacement + CSS modules
Lazy CSS: Must be red, but turn green when .
But turn orange, when . Additional clicks have no effect.
(HMR) Now modify the color in lazy.css and save it: This should remain orange.
Refresh and press buttons in reverse order: This should turn green instead.
Lazy CSS Module: Must be red, but turn green when .
Lazy CSS: Turn off the network and .
An error should have appeared.
Now if you turn the network back on and click it again, it should turn aqua.
Preloaded CSS: Must be green.
displays an alert but has no styling effect.
Preloaded inlined CSS: Must be green.
displays an alert but has no styling effect.
CrossOriginLoading Option: Must be red.
loads chunks with crossorigin attribute and should turn green.


- BNB Price:
- $338.98
- Pairs:
- $338.98
- Fees (24hr):
- $338.98
| Symbol |
Liquidity |
Fees (24hr) |
Price |
Price Change (24hrs) |
| Dakota Rice |
Niger |
Oud-Turnhout |
$36,738 |
$36,738 |
| Minerva Hooper |
CuraÃ§ao |
Sinaai-Waas |
$23,789 |
$36,738 |
| Sage Rodriguez |
Netherlands |
Baileux |
$56,142 |
$36,738 |
| Philip Chaney |
Korea, South |
Overland Park |
$38,735 |
$36,738 |
| Doris Greene |
Malawi |
Feldkirchen in KÃ¤rnten |
$63,542 |
$36,738 |
| Mason Porter |
Chile |
Gloucester |
$78,615 |
$36,738 |
| Jon Porter |
Portugal |
Gloucester |
$36,738 |
$36,738 |
| Name |
Liquidity |
Volume (24hrs) |
Volume (7d) |
Fees (24hr) |
1y Fees / Liquidity |
| Dakota Rice |
Niger |
Oud-Turnhout |
$36,738 |
$36,738 |
$36,738 |
| Minerva Hooper |
CuraÃ§ao |
Sinaai-Waas |
$23,789 |
$36,738 |
$36,738 |
| Sage Rodriguez |
Netherlands |
Baileux |
$56,142 |
$36,738 |
$36,738 |
| Philip Chaney |
Korea, South |
Overland Park |
$38,735 |
$36,738 |
$36,738 |
| Doris Greene |
Malawi |
Feldkirchen in KÃ¤rnten |
$63,542 |
$36,738 |
$36,738 |
| Mason Porter |
Chile |
Gloucester |
$78,615 |
$36,738 |
$36,738 |
| Jon Porter |
Portugal |
Gloucester |
$98,615 |
$36,738 |
$36,738 |
| Name |
Total Value |
Token Amount |
Token Amount |
Account |
Time |
| Dakota Rice |
Niger |
Oud-Turnhout |
$36,738 |
$36,738 |
$36,738 |
| Minerva Hooper |
CuraÃ§ao |
Sinaai-Waas |
$23,789 |
$36,738 |
$36,738 |
| Sage Rodriguez |
Netherlands |
Baileux |
$56,142 |
$36,738 |
$36,738 |
| Philip Chaney |
Korea, South |
Overland Park |
$38,735 |
$36,738 |
$36,738 |
| Doris Greene |
Malawi |
Feldkirchen in KÃ¤rnten |
$63,542 |
$36,738 |
$36,738 |
| Mason Porter |
Chile |
Gloucester |
$78,615 |
$36,738 |
$36,738 |
| Jon Porter |
Portugal |
Gloucester |
$98,615 |
$36,738 |
$36,738 |


cmsxa Module
Assembly Code Processing Library
Grammars
Lexical Assembly Grammar
Assembly Grammar
Namespaces
AssemblyTokens
cmsx
assembly
Files
cmsxa/Assembler.cm
cmsxa/Assembly.cm
cmsxa/AssemblyClassMap.cm
cmsxa/AssemblyInstruction.cm
cmsxa/AssemblyLexer.cm
cmsxa/AssemblyLexer.lexer
cmsxa/AssemblyParser.cm
cmsxa/AssemblyParser.parser
cmsxa/AssemblyTokens.cm
cmsxa/Visitor.cm
Classes
public
static
class
AssemblyClassMap
public
class
AssemblyLexer
public
static
class
AssemblyParser


Amazon Fire TV Blaster brings Alexa to your entertainment devices
by Rei PadlaA few months ago, Amazon announced new Fire TV products. Included were the first Fire TV Edition soundbar, a new Fire TV Cube, and more Fire TV models like the Fire TV Cube. Amazon isn’t content with all those products yet. It’s on fire as it just introduced another one–the Amazon Fire TV Blaster. The name alone makes it one kick-ass item that works as hands-free voice control for other home products that are not yet smart like a TV, cable, soundbar, satellite box, or a receiver.
The Amazon Fire TV Blaster still requires Fire TV Stick with Alexa Voice Remote, a 3d-gen Fire TV, Fire TV Stick 4K, or an Echo Smart display or speaker. It still needs a compatible Amazon device to be really “smart”.
With voice control, you can have control over the power, playback, volume, or switch between inputs. You don’t need to have multiple remotes with you because the Fire TV Blaster may be enough to control most entertainment devices. You are provided access to free Fire TV and Alexa apps on a Fire tablet, iOS 10 devices, or at least Android 5.0 units.
The Amazon Fire TV Blaster is only 2-inch tall. It is very small but offers big possibilities. Use with any compatible device for voice control courtesy of Alexa. With this tiny Fire TV Blaster, your Fire TV home entertainment can be further upgraded.
Voice support can be either near-field or far-field with compatible Echo and Fire TV products. It can control numerous IR-enabled devices from TVs to soundbars, A/V receivers, and cable boxes among others. It allows a seamless wireless connection between Fire and Echo devices.
The Fire TV Blaster is now listed on Amazon. The price tag reads $34. Free shipping is offered but the market release will start on December 11, Wednesday.


(common, js, jvm)
The offset-based paging object is a container for a set of objects. It contains a key called items
(whose value is an array of the requested objects) along with other keys like previous, next and
limit that can be useful in future calls.
(common, js, jvm)
Represents an Album on Spotify
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about one or more albums from the Spotify catalog.
(common, js, jvm)
Album search type
(common, js, jvm)
Represents a Spotify Album URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
(common, js, jvm)
Represents an Artist (distinct from a regular user) on Spotify
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about one or more artists from the Spotify catalog.
(common, js, jvm)
(common, js, jvm)
Represents a Spotify Artist URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
(common, js, jvm)
The Audio Analysis endpoint provides low-level audio analysis for all of the tracks
in the Spotify catalog. The Audio Analysis describes the track’s structure
and musical content, including rhythm, pitch, and timbre. All information is
precise to the audio sample. Many elements of analysis include confidence values,
a floating-point number ranging from 0.0 to 1.0. Confidence indicates the reliability
of its corresponding attribute. Elements carrying a small confidence value should
be considered speculative. There may not be sufficient data in the audio to
compute the attribute with high certainty.
(common, js, jvm)
Information about the analysis run
(common, js, jvm)
General attributes of a Track
(common, js, jvm)
Sections are defined by large variations in rhythm or timbre, e.g. chorus, verse, bridge, guitar solo, etc.
Each section contains its own descriptions of tempo, key, mode, time_signature, and loudness.*
(common, js, jvm)
Audio segments attempts to subdivide a song into many segments, with each segment containing
a roughly consistent sound throughout its duration.
(common, js, jvm)
An exception during the authentication process
(common, js, jvm)
The type of Spotify authorization used to build an Api instance
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
Endpoints for getting playlists and new album releases featured on Spotify’s Browse tab.
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
These endpoints allow you manage the artists, users and playlists that a Spotify user follows.
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about, and managing, tracks and albums that the current user has saved in their “Your Music” library.
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about the user’s listening habits.
(common, js, jvm)
(common, js, jvm)
These endpoints allow for viewing and controlling user playback. Please view the official documentation
for more information on how this works. This is in beta and is available for premium users only. Endpoints are not guaranteed to work and are subject to change!
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about a user’s playlists and for managing a user’s playlists.
(common, js, jvm)
Endpoints for retrieving information about a user’s profile.
(common, js, jvm)
Get Spotify catalog information about artists, albums, tracks, playlists, episodes, or shows that match a keyword string.
(common, js, jvm)
(common, js, jvm)
(jvm) (extensions in package com.adamratzman.spotify.utils)
(common, js, jvm)
Puts an object in-context by linking to other related endpoints
(common, js, jvm)
Copyright statement type of an Album
(common, js, jvm)
Represents a core Spotify object such as a Track or Album
(common, js, jvm)
(common, js, jvm)
Information about the current playback
(common, js, jvm)
Information about the currently playing track and context
(common, js, jvm)
The object type of the currently playing item
(common, js, jvm)
The cursor to use as key to find the next (or previous) page of items.
(common, js, jvm)
The cursor-based paging object is a container for a set of objects. It contains a key called
items (whose value is an array of the requested objects) along with other keys like next and
cursors that can be useful in future calls.
(common, js, jvm)
A device which is connected to the Spotify user
(common, js, jvm)
Electronic type of registered Spotify device
(common, js, jvm)
Maps a playback action to whether the user is disallowed from doing it
(common, js, jvm)
An episode (podcast) on Spotify
(common, js, jvm)
Endpoints for retrieving information about one or more episodes from the Spotify catalog.
(common, js, jvm)
(common, js, jvm)
An endpoint exception from Spotify
(common, js, jvm)
Wraps around ErrorObject. Serialized raw Spotify error response
(common, js, jvm)
(common, js, jvm)
An external id linked to the result object
(common, js, jvm)
Key/value pair mapping a name to an arbitrary url
(common, js, jvm)
Spotify featured playlists (on the Browse tab)
(common, js, jvm)
Information about a Spotify user's followers
(common, js, jvm)
(common, js, jvm)
This endpoint allow you check the playlists that a Spotify user follows.
(common, js, jvm)
Represents a generic instance of the Spotify API client, with common functionality and information between
implementations of the API
(js) (extensions in package com.adamratzman.spotify.utils)
(common, js, jvm)
Provides a fast, easy, and slim way to execute and retrieve HTTP GET, POST, PUT, and DELETE requests
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
Represents an identifiable Spotify object such as an Album or Recommendation Seed
(common, js, jvm)
Represents an identifiable Spotify object such as an Album or Recommendation Seed
(common, js, jvm)
(common, js, jvm)
Spotify Api builder interface
(common, js, jvm)
App Api builder interface
(common, js, jvm)
Client interface exposing getAuthorizationUrl
(common, js, jvm)
ISO 639-1
language code.
(common, js, jvm)
Type of object in a user's Spotify library
(common, js, jvm)
Represents a relinked track. This is playable in the
searched market. If null, the API result is playable in the market.
(common, js, jvm)
Locale code.
(common, js, jvm)
Local track object that representing a song uploaded from a client locally
(common, js, jvm)
Represents a Spotify local track URI
(common, js, jvm)
(common, js, jvm)
ISO 3166-1 country code.
(common, js, jvm)
Provide access to the underlying SpotifyApi
(common, js, jvm)
The offset-based nullable paging object is a container for a set of objects. It contains a key called items
(whose value is an array of the requested objects) along with other keys like previous, next and
limit that can be useful in future calls. Its items are not guaranteed to be not null
(common, js, jvm)
The offset-based non-nullable paging object is a container for a set of objects. It contains a key called items
(whose value is an array of the requested objects) along with other keys like previous, next and
limit that can be useful in future calls.
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
Action a user takes that will affect current playback
(common, js, jvm)
List of playback actions (pause, resume, etc) which a user is disallowed or allowed to do. Playback actions
NOT in disallows are allowed.
(common, js, jvm)
Information about a previously-played track
(common, js, jvm)
Context in which a track was played
(common, js, jvm)
Represents a Playlist on Spotify
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about a user’s playlists
(common, js, jvm)
Contains the snapshot id, returned from API responses
(common, js, jvm)
Represents a Spotify track inside a Playlist
(common, js, jvm)
A collection containing a link ( href ) to the Web API endpoint where full details of the playlist’s tracks
can be retrieved, along with the total number of tracks in the playlist.
(common, js, jvm)
Represents a Spotify Playlist URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
Seed from which the recommendation was constructed
(common, js, jvm)
(common, js, jvm)
Represents a response for which a relinked track could be available
(common, js, jvm)
How and if playback is repeating
(common, js, jvm)
Contains an explanation of why a track is not available
(common, js, jvm)
Interface that allows easy identifier retrieval for children with an implemented identifier
(common, js, jvm)
Represents the user’s most recent position in the episode. Set if the supplied access token is a user token and has
the scope SpotifyScope.USER_READ_PLAYBACK_POSITION.
(common, js, jvm)
Represents an album saved in a user's library
(common, js, jvm)
Represents a track saved in a user's library
(common, js, jvm)
(common, js, jvm)
Get Spotify catalog information about artists, albums, tracks or playlists that match a keyword string.
It is possible to have 0 results and no exception thrown with these methods. Check the size of items returned.
(common, js, jvm)
Information about a Spotify show, including its episodes
(common, js, jvm)
Endpoints for retrieving information about one or more shows and their episodes from the Spotify catalog.
(common, js, jvm)
(common, js, jvm)
Simplified Album object that can be used to retrieve a full Album
(common, js, jvm)
Simplified Artist object that can be used to retrieve a full Artist
(common, js, jvm)
A simplified episode (podcast) on Spotify
(common, js, jvm)
Local album object that goes with LocalTrack - represents the local album it was obtained from (likely "Local Files")
(common, js, jvm)
Local artist object (goes with LocalTrack) representing an artist on a local track
(common, js, jvm)
Simplified Playlist object that can be used to retrieve a full Playlist
(common, js, jvm)
Basic information about a Spotify show
(common, js, jvm)
Simplified Playlist object that can be used to retrieve a full Playlist
(common, js, jvm)
Represents an instance of the Spotify API client, with common
functionality and information between the SpotifyClientApi and SpotifyAppApi
implementations of the API
(common, js, jvm)
(common, js, jvm)
Spotify API builder
(common, js, jvm)
API Utilities
(common, js, jvm)
API Utilities
(common, js, jvm)
An API instance created with application credentials, not through
client authentication
(common, js, jvm)
(common, js, jvm)
SpotifyAppApi builder for api creation using client authorization
(common, js, jvm)
(common, js, jvm)
Spotify music category
(common, js, jvm)
An API instance created through client authentication, with access to private information
managed through the scopes exposed in token
(common, js, jvm)
(common, js, jvm)
SpotifyClientApi builder for api creation using client authorization
(common, js, jvm)
Describes an album's copyright information
(common, js, jvm)
(common, js, jvm)
A holder for application-specific credentials
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
A Spotify image
(common, js, jvm)
An API instance created through implicit grant flow, with access to private information
managed through the scopes exposed in token. token is not refreshable and is only accessible for limited time.
(common, js, jvm)
(common, js, jvm)
Public information about a Spotify user
(common, js, jvm)
Thrown when SpotifyApi.retryWhenRateLimited is false and requests have been ratelimited
(common, js, jvm)
(common, js, jvm)
Provides a uniform interface to retrieve, whether synchronously or asynchronously, T from Spotify
(common, js, jvm)
(common, js, jvm)
Scopes provide Spotify users using third-party apps the confidence
that only the information they choose to share will be shared, and nothing more.
(common, js, jvm)
(common, js, jvm)
Represents the positions inside a playlist's items list of where to locate the track
(common, js, jvm)
Represents a Spotify Track URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
Represents a Spotify URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
Exception instantiating or deserializing a uri perceived as invalid
(common, js, jvm)
User-defined authorization parameters
(common, js, jvm)
Authentication methods
(common, js, jvm)
Private information about a Spotify user. Each field may require a specific scope.
(common, js, jvm)
(common, js, jvm)
(common, js, jvm) (extensions in package com.adamratzman.spotify.utils)
(common, js, jvm)
This is a generic object used to represent various time intervals within Audio Analysis.
(common, js, jvm)
(common, js, jvm)
Represents a Spotify Token, retrieved through instantiating a SpotifyApi
(common, js, jvm)
(common, js, jvm)
Represents a music track on Spotify
(common, js, jvm)
General information about the track as a whole
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about one or more tracks from the Spotify catalog.
(common, js, jvm)
The track attribute wrapper contains a set value for a specific TuneableTrackAttribute
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
(common, js, jvm)
Describes a track attribute
(common, js, jvm)
(common, js, jvm)
Endpoints for retrieving information about a user’s profile.
(common, js, jvm)
Represents a Spotify User URI, parsed from either a Spotify ID or taken from an endpoint.
(common, js, jvm)
(common, js, jvm)