DATE
22-26 April 2019
22-26 April 2019
BTKIT, Dwarahat.
09
Open for All
Geographically, we are located amidst the hills of North India. To some, we might merely be a valley in Himalayas but we speak Technology. We are the Coding Club of BTKIT Dwarahat, Uttarakhand and we proudly present Kaivalya 2K19
Bipin Tripathi Kumaun Institute of Technology, Dwarahat is a Technical Institute of Uttarakhand Government that is situated near the hills of District Almora. “Kaivalya” is the annual technical fest organized by the Coding Club of BTKIT Dwarahat which is supported by BTKIT IEEE Student Branch.
After the grand success and the overwhelming response to “Kaivalya 2014, 2015, 2018”, the
only option we had was to make it bigger and better without compromising the convenience of participation and we have focussed on doing exactly that. All the major events of Kaivalya 2K19 will be hosted online and will be free to participate. The rewards have become more exciting and the challenges more promising. The speciality with Kaivalya 2K19 is that we, the organizers not only treat it as an event for you to interact with us but also for us to mingle up with you. In fact, it all started as a dream, and it eventually has become a community.
With exciting prizes and no participation fees, and the convenience of participating just with an internet connection from anywhere in the world, you have nothing to lose. So, find the event that suits you the most, enrol yourself and gear up, because the challenges are only going to get better and harder.
The motive of “Kaivalya” is to find developers among college students because Kaivalya is (and always will be) free and online, it will always attract plenty of young developers, from communities like codechef.com and hackerearth.com
“Gambit 2014” was the signature event of “Kaivalya 2014” and it being a very novice start up at that time received the participation of 174 teams from India. Same success happens in 2015 and 2018 and we are planning to make it again in 2019. Besides, online participation, BTKIT Dwarahat also is a source of huge onsite participation. Not only the competitions are organized but a lot of workshops are done by senior students and faculty members for juniors so that their interest in computer science field increase.
Talk is cheap; Show me the Code
The client may be the king, but he is not the art director
Idea is blunt, until it is presented
Coding with a twist
C is quirky, flawed, and an enormous success
Be a wise guy, Give math a try
The more you know, the more you grow
Darn the wheel of world in reverse gear!!
Clear vision holds the key
| Events Calendar | ||||
|---|---|---|---|---|
| 18-25 April |
Presentech02 PMVenue: Online |
Read More | Register Now! | |
| 22-25 April |
Cicada 330109.30 AMVenue: Online |
Read More | Register Now! | |
| 22 April |
Gambit10 AM - 01 PMVenue: CCFC |
Read More | Register Now! | |
| 22-25 April |
DEV.INC.CO02 PMVenue: CCFC |
Read More | Register Now! | |
| 22 April |
Andhadhun02.30 PM - 04.30 PMVenue: CCFC |
Read More | Register Now! | |
| 22 April |
Know Your Buddy04.45 PM - 05.45 PMVenue: CCFC |
Read More | Register Now! | |
| 25 April |
Mathematica10 AM - 12 PMVenue: CCFC |
Read More | Register Now! | |
| 25 April |
Track Back02 PM - 04 PMVenue: CCFC |
Read More | Register Now! | |
| 25 April |
Monkhub04 PM - 06 PMVenue: CCFC |
Read More | Register Now! | |
Copyright © Developed By Kuldeep Upreti | All rights reserved


{% extends 'base.html' %} {% load static %} {% block content %}
| Write : | |
|---|---|
| Date : | |
| Comment : | |
| File Attechment : | Attechment |
| Name | Parent Contact | US Branch | School | Monthly Report | Sent Date | Report to Parents Date | Comments | |||
|---|---|---|---|---|---|---|---|---|---|---|
| {{student.name}} | {{student.parent_info.wechat}} | {{student.school.provider_branch.name}} | {{student.school}} | {% if student.found_report.report_to_parent %}Complete | {% else %}Incomplete | {% endif %} {% if student.found_report.send_to_agent_date %}{{student.found_report.send_to_agent_date|date:"Y-m-d"}} | {% else%}{% endif %} {% if student.found_report.report_to_parent %} | {{student.found_report.report_to_parent|date:"Y-m-d"}} | {% else %}{% endif %} |


-
Creating Contents
Description
This Demo creates a Worksheet from the scratch.
-
Edit/ReadOnly Mode
Description
This Demo demonstrates the functioning of "Edit" and "Read Only" Modes of GridWeb Control.
-
Worksheets
Description
The Demo exhibits the manipulation (Add, Remove, Copy) of Sheets.
-
Cells
Description
The Demo exhibits the manipulation (Insertion, Deletion) of rows/columns, merging cells and adding/removing comments.
-
HeaderBar & CommandButton
Description
This Demo covers some useful properties of GridWeb Control.
-
FreezePane Report
Description
This Demo Imports an Excel File from a source and demonstrates Freezing Panes.
-
Freeze/Unfreeze Panes
Description
This Demo expresses customized Freezing Panes.
-
Hyperlink & CellImage
Description
The Demo presents the functionality of Hyperlink and CellImage Object.
-
Custom Headers
Description
This Demo customizes the Labels of Column Headers.
-
Math
Description
This Demo presents the exercise of Mathematical Functions.
-
Text & Data
Description
The Demo covers the practice session of String Functions.
-
Statistical
Description
The Demo presents the exercise of Statistical Functions.
-
Date & Time
Description
The Demo presents the exercise of Date and Time Functions.
-
Logical
Description
This Demo presents the Demonstration of Logical Functions.
-
Custom Format
Description
This Demo presents an exercise of Custom Formats.
-
Date & Time Format
Description
This Demo covers the exercise of Date and Time Formats.
-
Skins
Description
This Demo covers the Demonstration of GridWeb Control’s preset styles and custom styles.
-
Protection/Validation
Description
This Demo introduces the Data Validation capabilities of GridWeb Control.
-
Paginating Sheet
Description
This Demo Imports an Excel File from a source and divides the contents of the sheet into different pages.
-
Sort
Description
The Demo represents the sorting capabilities of GriWeb Control.
-
AutoFilter
Description
This Demo Imports an Excel File from a source and Set the AutoFilter feature.
-
Handling Events
Description
This Demo Demonstrates Event Handling related to GridWeb Control.
-
load large rows in async way
Description
This demo loads a file with many rows,every time scroll it will load piece of rows for rendering.
-
use client js function
Description
trigger client js call back on ajax after cell select,check console out put.
-
show controls
Description
display kind of controls.
-
show group row and columns
Description
This demo loads an existing file with group row and columns.
-
show condition format
Description
This demo shows Condition Format style .
-
show chart
Description
This demo shows chart .
-
show pivot
Description
This demo shows show pivot .


Conv1D
keras.layers.Conv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If use_bias
is True, a bias vector is created and added to the outputs.
Finally, if activation
is not None
,
it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide an input_shape
argument (tuple of integers or None
, does not
include the batch axis), e.g. input_shape=(10, 128)
for time series
sequences of 10 time steps with 128 features per step in
data_format="channels_last"
, or (None, 128)
for variable-length
sequences with 128 features per step.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
- strides: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: One of
"valid"
,"causal"
or"same"
(case-insensitive)."valid"
means "no padding"."same"
results in padding the input such that the output has the same length as the original input."causal"
results in causal (dilated) convolutions, e.g.output[t]
does not depend oninput[t + 1:]
. A zero padding is used such that the output has the same length as the original input. Useful when modeling temporal data where the model should not violate the temporal order. See WaveNet: A Generative Model for Raw Audio, section 2.1. - data_format: A string,
one of
"channels_last"
(default) or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, steps, channels)
(default format for temporal data in Keras) while"channels_first"
corresponds to inputs with shape(batch, channels, steps)
. - dilation_rate: an integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
3D tensor with shape: (batch, steps, channels)
Output shape
3D tensor with shape: (batch, new_steps, filters)
steps
value might have changed due to padding or strides.
Conv2D
keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If use_bias
is True,
a bias vector is created and added to the outputs. Finally, if
activation
is not None
, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument input_shape
(tuple of integers, does not include the batch axis),
e.g. input_shape=(128, 128, 3)
for 128x128 RGB pictures
in data_format="channels_last"
.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). Note that"same"
is slightly inconsistent across backends withstrides
!= 1, as described here - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
4D tensor with shape:
(batch, channels, rows, cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, rows, cols, channels)
if data_format
is "channels_last"
.
Output shape
4D tensor with shape:
(batch, filters, new_rows, new_cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, new_rows, new_cols, filters)
if data_format
is "channels_last"
.
rows
and cols
values might have changed due to padding.
SeparableConv1D
keras.layers.SeparableConv1D(filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None)
Depthwise separable 1D convolution.
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 depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
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.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of single integer, specifying the length of the 1D convolution window.
- strides: An integer or tuple/list of single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, steps, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, steps)
. - dilation_rate: An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. - depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to
filters_in * depth_multiplier
. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- pointwise_initializer: Initializer for the pointwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- pointwise_regularizer: Regularizer function applied to the pointwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- pointwise_constraint: Constraint function applied to the pointwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
3D tensor with shape:
(batch, channels, steps)
if data_format
is "channels_first"
or 3D tensor with shape:
(batch, steps, channels)
if data_format
is "channels_last"
.
Output shape
3D tensor with shape:
(batch, filters, new_steps)
if data_format
is "channels_first"
or 3D tensor with shape:
(batch, new_steps, filters)
if data_format
is "channels_last"
.
new_steps
values might have changed due to padding or strides.
SeparableConv2D
keras.layers.SeparableConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None)
Depthwise separable 2D convolution.
Separable convolution performs first
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
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.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. - depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to
filters_in * depth_multiplier
. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- pointwise_initializer: Initializer for the pointwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- pointwise_regularizer: Regularizer function applied to the pointwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- pointwise_constraint: Constraint function applied to the pointwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
4D tensor with shape:
(batch, channels, rows, cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, rows, cols, channels)
if data_format
is "channels_last"
.
Output shape
4D tensor with shape:
(batch, filters, new_rows, new_cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, new_rows, new_cols, filters)
if data_format
is "channels_last"
.
rows
and cols
values might have changed due to padding.
DepthwiseConv2D
keras.layers.DepthwiseConv2D(kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None)
Depthwise 2D convolution.
Depthwise convolution performs
just the first step of a depthwise spatial convolution
(which acts on each input channel separately).
The depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
Arguments
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to
filters_in * depth_multiplier
. - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be 'channels_last'. - dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. 'linear' activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- depthwise_initializer: Initializer for the depthwise kernel matrix (see initializers).
- bias_initializer: Initializer for the bias vector (see initializers).
- depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see regularizer).
- bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its 'activation'). (see regularizer).
- depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
4D tensor with shape:
(batch, channels, rows, cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, rows, cols, channels)
if data_format
is "channels_last"
.
Output shape
4D tensor with shape:
(batch, channels * depth_multiplier, new_rows, new_cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, new_rows, new_cols, channels * depth_multiplier)
if data_format
is "channels_last"
.
rows
and cols
values might have changed due to padding.
Conv2DTranspose
keras.layers.Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
Transposed convolution layer (sometimes called Deconvolution).
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.
When using this layer as the first layer in a model,
provide the keyword argument input_shape
(tuple of integers, does not include the batch axis),
e.g. input_shape=(128, 128, 3)
for 128x128 RGB pictures
in data_format="channels_last"
.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - output_padding: An integer or tuple/list of 2 integers,
specifying the amount of padding along the height and width
of the output tensor.
Can be a single integer to specify the same value for all
spatial dimensions.
The amount of output padding along a given dimension must be
lower than the stride along that same dimension.
If set to
None
(default), the output shape is inferred. - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
4D tensor with shape:
(batch, channels, rows, cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, rows, cols, channels)
if data_format
is "channels_last"
.
Output shape
4D tensor with shape:
(batch, filters, new_rows, new_cols)
if data_format
is "channels_first"
or 4D tensor with shape:
(batch, new_rows, new_cols, filters)
if data_format
is "channels_last"
.
rows
and cols
values might have changed due to padding.
If output_padding
is specified:
new_rows = ((rows - 1) * strides[0] + kernel_size[0]
- 2 * padding[0] + output_padding[0])
new_cols = ((cols - 1) * strides[1] + kernel_size[1]
- 2 * padding[1] + output_padding[1])
References
Conv3D
keras.layers.Conv3D(filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format=None, dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If use_bias
is True,
a bias vector is created and added to the outputs. Finally, if
activation
is not None
, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument input_shape
(tuple of integers, does not include the batch axis),
e.g. input_shape=(128, 128, 128, 1)
for 128x128x128 volumes
with a single channel,
in data_format="channels_last"
.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 3 integers,
specifying the strides of the convolution along each spatial dimension.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - dilation_rate: an integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
5D tensor with shape:
(batch, channels, conv_dim1, conv_dim2, conv_dim3)
if data_format
is "channels_first"
or 5D tensor with shape:
(batch, conv_dim1, conv_dim2, conv_dim3, channels)
if data_format
is "channels_last"
.
Output shape
5D tensor with shape:
(batch, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)
if data_format
is "channels_first"
or 5D tensor with shape:
(batch, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)
if data_format
is "channels_last"
.
new_conv_dim1
, new_conv_dim2
and new_conv_dim3
values might have
changed due to padding.
Conv3DTranspose
keras.layers.Conv3DTranspose(filters, kernel_size, strides=(1, 1, 1), padding='valid', output_padding=None, data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
Transposed convolution layer (sometimes called Deconvolution).
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.
When using this layer as the first layer in a model,
provide the keyword argument input_shape
(tuple of integers, does not include the batch axis),
e.g. input_shape=(128, 128, 128, 3)
for a 128x128x128 volume with 3 channels
if data_format="channels_last"
.
Arguments
- filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
- kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
- strides: An integer or tuple/list of 3 integers,
specifying the strides of the convolution
along the depth, height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. - padding: one of
"valid"
or"same"
(case-insensitive). - output_padding: An integer or tuple/list of 3 integers,
specifying the amount of padding along the depth, height, and
width.
Can be a single integer to specify the same value for all
spatial dimensions.
The amount of output padding along a given dimension must be
lower than the stride along that same dimension.
If set to
None
(default), the output shape is inferred. - data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, depth, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, depth, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - dilation_rate: an integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. - activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
5D tensor with shape:
(batch, channels, depth, rows, cols)
if data_format
is "channels_first"
or 5D tensor with shape:
(batch, depth, rows, cols, channels)
if data_format
is "channels_last"
.
Output shape
5D tensor with shape:
(batch, filters, new_depth, new_rows, new_cols)
if data_format
is "channels_first"
or 5D tensor with shape:
(batch, new_depth, new_rows, new_cols, filters)
if data_format
is "channels_last"
.
depth
and rows
and cols
values might have changed due to padding.
If output_padding
is specified::
new_depth = ((depth - 1) * strides[0] + kernel_size[0]
- 2 * padding[0] + output_padding[0])
new_rows = ((rows - 1) * strides[1] + kernel_size[1]
- 2 * padding[1] + output_padding[1])
new_cols = ((cols - 1) * strides[2] + kernel_size[2]
- 2 * padding[2] + output_padding[2])
References
Cropping1D
keras.layers.Cropping1D(cropping=(1, 1))
Cropping layer for 1D input (e.g. temporal sequence).
It crops along the time dimension (axis 1).
Arguments
- cropping: int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). If a single int is provided, the same value will be used for both.
Input shape
3D tensor with shape (batch, axis_to_crop, features)
Output shape
3D tensor with shape (batch, cropped_axis, features)
Cropping2D
keras.layers.Cropping2D(cropping=((0, 0), (0, 0)), data_format=None)
Cropping layer for 2D input (e.g. picture).
It crops along spatial dimensions, i.e. height and width.
Arguments
- cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric cropping is applied to height and width.
- If tuple of 2 ints:
interpreted as two different
symmetric cropping values for height and width:
(symmetric_height_crop, symmetric_width_crop)
. - If tuple of 2 tuples of 2 ints:
interpreted as
((top_crop, bottom_crop), (left_crop, right_crop))
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, rows, cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, rows, cols)
Output shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, cropped_rows, cropped_cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, cropped_rows, cropped_cols)
Examples
# Crop the input 2D images or feature maps
model = Sequential()
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
input_shape=(28, 28, 3)))
# now model.output_shape == (None, 24, 20, 3)
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
# now model.output_shape == (None, 20, 16, 64)
Cropping3D
keras.layers.Cropping3D(cropping=((1, 1), (1, 1), (1, 1)), data_format=None)
Cropping layer for 3D data (e.g. spatial or spatio-temporal).
Arguments
- cropping: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
- If int: the same symmetric cropping is applied to depth, height, and width.
- If tuple of 3 ints:
interpreted as three different
symmetric cropping values for depth, height, and width:
(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)
. - If tuple of 3 tuples of 2 ints:
interpreted as
((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop,
depth)
- If data_format
is "channels_first"
:
(batch, depth,
first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)
Output shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis,
depth)
- If data_format
is "channels_first"
:
(batch, depth,
first_cropped_axis, second_cropped_axis, third_cropped_axis)
UpSampling1D
keras.layers.UpSampling1D(size=2)
Upsampling layer for 1D inputs.
Repeats each temporal step size
times along the time axis.
Arguments
- size: integer. Upsampling factor.
Input shape
3D tensor with shape: (batch, steps, features)
.
Output shape
3D tensor with shape: (batch, upsampled_steps, features)
.
UpSampling2D
keras.layers.UpSampling2D(size=(2, 2), data_format=None, interpolation='nearest')
Upsampling layer for 2D inputs.
Repeats the rows and columns of the data by size[0] and size[1] respectively.
Arguments
- size: int, or tuple of 2 integers. The upsampling factors for rows and columns.
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". - interpolation: A string, one of
nearest
orbilinear
. Note that CNTK does not support yet thebilinear
upscaling and that with Theano, onlysize=(2, 2)
is possible.
Input shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, rows, cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, rows, cols)
Output shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, upsampled_rows, upsampled_cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, upsampled_rows, upsampled_cols)
UpSampling3D
keras.layers.UpSampling3D(size=(2, 2, 2), data_format=None)
Upsampling layer for 3D inputs.
Repeats the 1st, 2nd and 3rd dimensions of the data by size[0], size[1] and size[2] respectively.
Arguments
- size: int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3.
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, dim1, dim2, dim3, channels)
- If data_format
is "channels_first"
:
(batch, channels, dim1, dim2, dim3)
Output shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)
- If data_format
is "channels_first"
:
(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)
ZeroPadding1D
keras.layers.ZeroPadding1D(padding=1)
Zero-padding layer for 1D input (e.g. temporal sequence).
Arguments
-
padding: int, or tuple of int (length 2), or dictionary.
- If int:
How many zeros to add at the beginning and end of the padding dimension (axis 1).
- If tuple of int (length 2):
How many zeros to add at the beginning and at the end of the padding dimension (
(left_pad, right_pad)
).
Input shape
3D tensor with shape (batch, axis_to_pad, features)
Output shape
3D tensor with shape (batch, padded_axis, features)
ZeroPadding2D
keras.layers.ZeroPadding2D(padding=(1, 1), data_format=None)
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.
Arguments
- padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric padding is applied to height and width.
- If tuple of 2 ints:
interpreted as two different
symmetric padding values for height and width:
(symmetric_height_pad, symmetric_width_pad)
. - If tuple of 2 tuples of 2 ints:
interpreted as
((top_pad, bottom_pad), (left_pad, right_pad))
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, height, width, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, rows, cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, rows, cols)
Output shape
4D tensor with shape:
- If data_format
is "channels_last"
:
(batch, padded_rows, padded_cols, channels)
- If data_format
is "channels_first"
:
(batch, channels, padded_rows, padded_cols)
ZeroPadding3D
keras.layers.ZeroPadding3D(padding=(1, 1, 1), data_format=None)
Zero-padding layer for 3D data (spatial or spatio-temporal).
Arguments
- padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
- If int: the same symmetric padding is applied to height and width.
- If tuple of 3 ints:
interpreted as three different
symmetric padding values for depth, height, and width:
(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)
. - If tuple of 3 tuples of 2 ints:
interpreted as
((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))
- data_format: A string,
one of
"channels_last"
or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while"channels_first"
corresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last".
Input shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad,
depth)
- If data_format
is "channels_first"
:
(batch, depth,
first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)
Output shape
5D tensor with shape:
- If data_format
is "channels_last"
:
(batch, first_padded_axis, second_padded_axis, third_axis_to_pad,
depth)
- If data_format
is "channels_first"
:
(batch, depth,
first_padded_axis, second_padded_axis, third_axis_to_pad)


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