Add input/outputShape information to all KDocs of layers (#211)

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/ELU.kt

@@ -21,6 +21,12 @@ import org.tensorflow.op.Ops
  * the activation closer to zero which enable faster learning as they
  * bring the gradient to the natural gradient.
  *
+ * __Input shape__:  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the samples axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape__: Same shape as the input.
+ *
  * @property [alpha] Hyperparameter that controls the value to which
  * an ELU saturates for negative net inputs. Should be > 0.
  * @constructor Creates [ELU] object.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/LeakyReLU.kt

@@ -16,6 +16,13 @@ import org.tensorflow.op.Ops
  * f(x) = x,                if x >= 0
  * f(x) = alpha * x         if x < 0
  * ```
+ *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the batch axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ 2D tensor with shape `(batch_size, channels)`.
+ *
  * @property [alpha] Negative slope coefficient. Should be >= 0.
  * @constructor Creates [LeakyReLU] object.
  * @since 0.3

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/PReLU.kt

@@ -26,6 +26,12 @@ import org.tensorflow.op.Ops
  * ```
  * where `alpha` is a learnable weight and has the same shape as `x` (i.e. input).
  *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the samples axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ Same shape as the input.
+ *
  * @property [alphaInitializer] Initializer instance for the weights.
  * @property [alphaRegularizer] Regularizer instance for the weights.
  * @property [sharedAxes] The axes along which to share learnable parameters.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/ReLU.kt

@@ -19,6 +19,13 @@ import org.tensorflow.op.Ops
  * f(x) = x,                               if threshold <= x < maxValue
  * f(x) = negativeSlope * (x - threshold), if x < threshold
  * ```
+ *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the batch axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ Same shape as the input.
+ *
  * @property [maxValue] Maximum activation value. Should be >= 0.
  * @property [negativeSlope] Negative slope coefficient. Should be >= 0.
  * @property [threshold] Threshold value for threshold activation.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/Softmax.kt

@@ -21,6 +21,12 @@ import org.tensorflow.op.core.ReduceSum
  * softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
  * ```
  *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the samples axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ Same shape as the input.
+ *
  * @property [axis] along which the softmax normalization is applied.
  * @constructor Creates [Softmax] object
  * @since 0.3

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/activation/ThresholdedReLU.kt

@@ -16,6 +16,13 @@ import org.tensorflow.op.Ops
  * f(x) = x,        if x > theta
  * f(x) = 0         otherwise
  * ```
+ *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the samples axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ Same shape as the input.
+ *
  * @property [theta] Threshold value for activation.
  * @constructor Creates [ThresholdedReLU] object.
  *

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/convolutional/DepthwiseConv2D.kt

@@ -30,6 +30,11 @@ import org.tensorflow.op.nn.DepthwiseConv2dNative
  * The `depthMultiplier` argument controls how many
  * output channels are generated per input channel in the depthwise step.
  *
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 4D tensor with shape `(batch_size, new_rows, new_cols, channels * depth_multiplier)`.
+ *                   `rows` and `cols` values might have changed due to padding.
+ *
  * @property [kernelSize] Two long numbers, specifying the height and width of the 2D convolution window.
  * @property [strides] Strides of the pooling operation for each dimension of input tensor.
  * NOTE: Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/convolutional/SeparableConv2D.kt

@@ -35,6 +35,11 @@ import kotlin.math.roundToInt
  * Intuitively, separable convolutions can be understood as
  * a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
  *
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 4D tensor with shape `(batch_size, new_rows, new_cols, filters)`.
+ *                   `rows` and `cols` values might have changed due to padding.
+ *
  * @property [filters] The dimensionality of the output space (i.e. the number of filters in the convolution).
  * @property [kernelSize] Two long numbers, specifying the height and width of the 2D convolution window.
  * @property [strides] Strides of the pooling operation for each dimension of input tensor.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/core/Dense.kt

@@ -31,6 +31,10 @@ import org.tensorflow.op.Ops
  * created by the layer, and `bias` is a bias vector created by the layer
  * (only applicable if `use_bias` is `True`).
  *
+ * __Input shape:__  2D tensor with shape `(batch_size, input_dim)`.
+ *
+ * __Output shape:__ 2D tensor with shape `(batch_size, units)`.
+ *
  * @property [outputSize] Dimensionality of the output space.
  * @property [activation] Activation function.
  * @property [kernelInitializer] Initializer function for the 'kernel' weights matrix.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/normalization/BatchNorm.kt

@@ -22,6 +22,12 @@ import org.tensorflow.op.core.Variable
 /**
  * NOTE: This layer is not trainable and does not update its weights. It's frozen by default.
  *
+ * __Input shape:__  Arbitrary. Use the keyword argument `input_shape`
+ *                   (tuple of integers, does not include the samples axis)
+ *                   when using this layer as the first layer in a model.
+ *
+ * __Output shape:__ Same shape as input.
+ *
  * @property [axis] Integer or a list of integers, the axis that should be normalized (typically the features' axis).
  * @property [momentum] Momentum for the moving average.
  * @property [center] If True, add offset of beta to normalized tensor. If False, beta is ignored.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/AvgPool1D.kt

@@ -17,6 +17,10 @@ import org.tensorflow.op.core.Squeeze
  *
  * Downsamples the input by taking the average over a temporal window of size [poolSize].
  *
+ * __Input shape:__  3D tensor with shape `(batch_size, steps, features)`.
+ *
+ * __Output shape:__ 3D tensor with shape `(batch_size, downsampled_steps, features)`.
+ *
  * @property [poolSize] Size of the temporal pooling window for each dimension of input.
  * @property [strides] The amount of shift for pooling window per each input dimension in each pooling step.
  * @property [padding] Padding strategy; can be either of [ConvPadding.VALID] which means no

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/AvgPool2D.kt

@@ -14,7 +14,9 @@ import org.tensorflow.op.Ops
 /**
  * Average pooling layer for 2D inputs (e.g. images).
  *
- * NOTE: Works with tensors which must have rank 4 (batch, height, width, channels).
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 4D tensor with shape `(batch_size, pooled_rows, pooled_cols, channels)`.
  *
  * @property [poolSize] The size of the sliding window for each dimension of input tensor (pool batch, pool height, pool width, pool channels).
  * Usually, pool batch and pool channels are equal to 1.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/AvgPool3D.kt

@@ -17,6 +17,10 @@ import org.tensorflow.op.Ops
  *
  * Downsamples the input by taking the average over a window of size [poolSize].
  *
+ * __Input shape:__  5D tensor with shape `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`.
+ *
+ * __Output shape:__ 5D tensor with shape `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`.
+ *
  * @property [poolSize] Size of the pooling window for each dimension of input.
  * @property [strides] The amount of shift for pooling window per each input dimension in each pooling step.
  * @property [padding] Padding strategy; can be either of [ConvPadding.VALID] which means no

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/GlobalAvgPool2D.kt

@@ -13,11 +13,9 @@ import org.tensorflow.op.Ops
 /**
  * Global average pooling operation for 2D data (images and so on).
  *
- * NOTE: Works with tensors which must have rank 4 (batch, height, width, channels).
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
  *
- * Input shape: 4D tensor with shape `(batch_size, rows, cols, channels)`.
- *
- * Output shape: 2D tensor with shape `(batch_size, channels)`.
+ * __Output shape:__ 2D tensor with shape `(batch_size, channels)`.
  *
  * @property [name] Custom layer name.
  * @constructor Creates [GlobalAvgPool2D] object.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/GlobalMaxPool1D.kt

@@ -14,6 +14,10 @@ import org.tensorflow.op.Ops
  *
  * Downsamples the input by taking the maximum value over time dimension.
  *
+ * __Input shape:__  3D tensor with shape `(batch_size, steps, features)`.
+ *
+ * __Output shape:__ 2D tensor with shape `(batch_size, features)`.
+ *
  * @since 0.3
  */
 public class GlobalMaxPool1D(

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/GlobalMaxPool2D.kt

@@ -14,6 +14,10 @@ import org.tensorflow.op.Ops
  *
  * Downsamples the input by taking the maximum value over spatial dimensions.
  *
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 2D tensor with shape `(batch_size, channels)`.
+ *
  * @since 0.3
  */
 public class GlobalMaxPool2D(

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/GlobalMaxPool3D.kt

@@ -14,6 +14,10 @@ import org.tensorflow.op.Ops
  *
  * Downsamples the input by taking the maximum value over spatio-temporal dimensions.
  *
+ * __Input shape:__  5D tensor with shape `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`.
+ *
+ * __Output shape:__ 2D tensor with shape `(batch_size, channels)`.
+ *
  * @since 0.3
  */
 public class GlobalMaxPool3D(

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/MaxPool1D.kt

@@ -17,6 +17,10 @@ import org.tensorflow.op.core.Squeeze
  *
  * Downsamples the input by taking maximum value over a temporal window of size [poolSize].
  *
+ * __Input shape:__  3D tensor with shape `(batch_size, steps, features)`.
+ *
+ * __Output shape:__ 3D tensor with shape `(batch_size, downsampled_steps, features)`.
+ *
  * @property [poolSize] Size of the temporal pooling window for each dimension of input.
  * @property [strides] The amount of shift for pooling window per each input dimension in each pooling step.
  * @property [padding] Padding strategy; can be either of [ConvPadding.VALID] which means no padding, or

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/MaxPool2D.kt

@@ -13,7 +13,9 @@ import org.tensorflow.op.Ops
 /**
  * Max pooling layer for 2D inputs (e.g. images).
  *
- * NOTE: Works with tensors which must have rank 4 (batch, height, width, channels).
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 4D tensor with shape `(batch_size, pooled_rows, pooled_cols, channels)`.
  *
  * @property [poolSize] The size of the sliding window for each dimension of input tensor (pool batch, pool height, pool width, pool channels).
  * Usually, pool batch and pool channels are equal to 1.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/pooling/MaxPool3D.kt

@@ -13,7 +13,11 @@ import java.util.*
 
 /**
  * Max pooling operation for 3D data (spatial or spatio-temporal).
- * NOTE: Works with tensors which must have rank 5 (batch, depth, height, width, channels).
+ *
+ * __Input shape:__  5D tensor with shape `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`.
+ *
+ * __Output shape:__ 5D tensor with shape: `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`.
+ *
  * @property [poolSize] The size of the sliding window for each dimension of input tensor (pool batch, pool depth ,pool height, pool width, pool channels).
  * Usually, pool batch and pool channels are equal to 1.
  * @property [strides] Strides of the pooling operation for each dimension of input tensor.

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/reshaping/ZeroPadding1D.kt

@@ -10,6 +10,11 @@ import org.tensorflow.Shape
 /**
  * Zero-padding layer for 1D input (e.g. audio).
  * This layer can add zeros in the rows of the audio tensor
+ *
+ * __Input shape:__  3D tensor with shape `(batch_size, axis_to_pad, features)`.
+ *
+ * __Output shape:__ 3D tensor with shape `(batch_size, padded_axis, features)`.
+ *
  * @property [padding] 2 numbers  interpreted as `(left_pad, right_pad)`.
  *
  * @since 0.3

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/reshaping/ZeroPadding2D.kt

@@ -12,6 +12,11 @@ import org.tensorflow.Shape
 /**
  * Zero-padding layer for 2D input (e.g. picture).
  * This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.
+ *
+ * __Input shape:__  4D tensor with shape `(batch_size, rows, cols, channels)`.
+ *
+ * __Output shape:__ 4D tensor with shape `(batch_size, padded_rows, padded_cols, channels)`.
+ *
  * @property [padding] 4 numbers  interpreted as `(top_pad, bottom_pad, left_pad, right_pad)`.
  */
 public class ZeroPadding2D : AbstractZeroPadding {

tensorflow/src/main/kotlin/org/jetbrains/kotlinx/dl/api/core/layer/reshaping/ZeroPadding3D.kt

@@ -10,6 +10,11 @@ import org.tensorflow.Shape
 /**
  * Zero-padding layer for 3D input (e.g. video).
  * This layer can add zeros in the rows, cols and depth of a video tensor.
+ *
+ * __Input shape:__  5D tensor with shape `(batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)`.
+ *
+ * __Output shape:__ 5D tensor with shape `(batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)`.
+ *
  * @property [padding] 6 numbers  interpreted as `(left_dim1_pad, right_dim1_pad, left_dim2_pad, right_dim2_pad, left_dim3_pad, right_dim3_pad)`.
  *
  * @since 0.3