prakashjayy

\begin{equation*}
\left[ \begin{array}{cccc}
i_{1} & i_{2} & i_{3} \\\end{array} \right] \times \left[ \begin{array}{cccc}
W_{i1j1} & W_{i1j2} & W_{i1j3} \\
W_{i2j1} & W_{i2j2} & W_{i2j3} \\
W_{i3j1} & W_{i3j2} & W_{i3j3} \\ \end{array} \right] = \left[ \begin{array}{cccc}
h_{1in} & h_{2in} & h_{3in} \\\end{array} \right]
\end{equation*}

prakashjayy

# This is the docker image available. I am using cpu version here. If needed there is gpu version available.
FROM bvlc/caffe:cpu

# Copy the file into docker
COPY requirements.txt requirements.txt

# Run the copied file
RUN pip install -r requirements.txt

# create a folder called model1 and copy all the files in the folder into that folder

prakashjayy

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import time
import os
import argparse

## Load the model 

prakashjayy

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256
train_data_dir = "data/train"

prakashjayy

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256

prakashjayy

# Fastscript usage under various instancees 
# https://github.com/fastai/fastscript/blob/master/00_core.ipynb
# https://fastcore.fast.ai/script.html

from fastcore.all import *

@call_parse 
def test_script(p: Param(help="any basic string", type=str)):
	print(len(p))

prakashjayy

def get_tile_name_path(dst_dir, index, city=city, code=code):
    '''
    generating specific tile name
    '''
    dst_tile_name = "{}_{}_{}.tif".format(city, code, str(index).zfill(5))
    dst_tile_path = os.path.join(dst_dir, dst_tile_name)
    return dst_tile_name, dst_tile_path

def get_tile_transform(parent_transform, pixel_x,pixel_y):
    '''

prakashjayy

@patch_to(ds.FeatureCollection, cls_method=True)
def read_generic(cls, 
                 fp: str,
                 dst_crs: Union[str, rasterio.crs.CRS]=None,
):
    '''
    geojson reader and can also be used to ensure the geojson in mapped to a destined crs
    '''
    with open(fp, 'r', encoding='utf-8') as f:
        collection = json.load(f)