Rishit Dagli Rishit-dagli

## 404-kittens.markdown

      
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                Rishit-dagli
                / 404-kittens.markdown
            
            
              Created
              February 14, 2022 06:32
            
              
                404 Kittens
              
          
    404 Kittens

This is how we pulled off our amazing 404 page.
A Pen by nclud team on CodePen.
License.

  
## view on github.md

      
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                Rishit-dagli
                / view on github.md
            
            
              Created
              August 31, 2021 12:21
            
              
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## main.py
import cv2
import numpy as np
from PIL import Image
import tensorflow as tf
import matplotlib.pyplot as plt
from skimage.transform import resize
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications import EfficientNetB4
from tensorflow.keras.applications import MobileNetV2

## skewness-and-kurtosis.ipynb

      
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                Rishit-dagli
                / skewness-and-kurtosis.ipynb
            
            
              Last active
              June 17, 2021 09:25
            
              
                Calculate skewness and kurtosis of data in Python
              
          
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## housing.csv
 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00
 0.02731   0.00   7.070  0  0.4690  6.4210  78.90  4.9671   2  242.0  17.80 396.90   9.14  21.60
 0.02729   0.00   7.070  0  0.4690  7.1850  61.10  4.9671   2  242.0  17.80 392.83   4.03  34.70
 0.03237   0.00   2.180  0  0.4580  6.9980  45.80  6.0622   3  222.0  18.70 394.63   2.94  33.40
 0.06905   0.00   2.180  0  0.4580  7.1470  54.20  6.0622   3  222.0  18.70 396.90   5.33  36.20
 0.02985   0.00   2.180  0  0.4580  6.4300  58.70  6.0622   3  222.0  18.70 394.12   5.21  28.70
 0.08829  12.50   7.870  0  0.5240  6.0120  66.60  5.5605   5  311.0  15.20 395.60  12.43  22.90
 0.14455  12.50   7.870  0  0.5240  6.1720  96.10  5.9505   5  311.0  15.20 396.90  19.15  27.10
 0.21124  12.50   7.870  0  0.5240  5.6310 100.00  6.0821   5  311.0  15.20 386.63  29.93  16.50
 0.17004  12.50   7.870  0  0.5240  6.0040  85.90  6.5921   5  311.0  15.20 386.71  17.10  18.90

## mpt-tf.py
import tensorflow as tf

policy = tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(policy)

inputs = keras.Input(shape=(784,))
x = tf.keras.layers.Dense(4096, activation='relu')(inputs)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = layers.Dense(10)(x)
outputs = layers.Activation('softmax', dtype='float32')(x)

## mixed-precision.py
import tensorflow as tf

policy = tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(policy)

inputs = keras.Input(shape=(784,))
x = tf.keras.layers.Dense(4096, activation='relu')(inputs)
x = tf.keras.layers.Dense(4096, activation='relu')(x)
x = layers.Dense(10)(x)
outputs = layers.Activation('softmax', dtype='float32')(x)

## caching.ipynb

      
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                Rishit-dagli
                / caching.ipynb
            
            
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              April 23, 2021 08:21
            
              
                See how you can use .cache in TensorFlow to boost your performance
              
          
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## random-tf.py
import kerastuner as kt
import tensorflow as tf

def model_builder(hp):
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))

  # Tune the number of units in the first Dense layer
  # Choose an optimal value between 32-512
  hp_units = hp.Int('units', min_value = 32, max_value = 512, step = 32)

## hyperband-tf.py
def model_builder(hp):
  model = keras.Sequential()
  model.add(keras.layers.Flatten(input_shape=(28, 28)))

  # Tune the number of units in the first Dense layer
  # Choose an optimal value between 32-512
  hp_units = hp.Int('units', min_value = 32, max_value = 512, step = 32)
  model.add(keras.layers.Dense(units = hp_units, activation = 'relu'))
  model.add(keras.layers.Dense(10))
	import cv2
	import numpy as np
	from PIL import Image
	import tensorflow as tf
	import matplotlib.pyplot as plt
	from skimage.transform import resize
	from tensorflow.keras.models import Model
	from tensorflow.keras.preprocessing.image import load_img, img_to_array
	from tensorflow.keras.applications import EfficientNetB4
	from tensorflow.keras.applications import MobileNetV2
	0.00632 18.00 2.310 0 0.5380 6.5750 65.20 4.0900 1 296.0 15.30 396.90 4.98 24.00
	0.02731 0.00 7.070 0 0.4690 6.4210 78.90 4.9671 2 242.0 17.80 396.90 9.14 21.60
	0.02729 0.00 7.070 0 0.4690 7.1850 61.10 4.9671 2 242.0 17.80 392.83 4.03 34.70
	0.03237 0.00 2.180 0 0.4580 6.9980 45.80 6.0622 3 222.0 18.70 394.63 2.94 33.40
	0.06905 0.00 2.180 0 0.4580 7.1470 54.20 6.0622 3 222.0 18.70 396.90 5.33 36.20
	0.02985 0.00 2.180 0 0.4580 6.4300 58.70 6.0622 3 222.0 18.70 394.12 5.21 28.70
	0.08829 12.50 7.870 0 0.5240 6.0120 66.60 5.5605 5 311.0 15.20 395.60 12.43 22.90
	0.14455 12.50 7.870 0 0.5240 6.1720 96.10 5.9505 5 311.0 15.20 396.90 19.15 27.10
	0.21124 12.50 7.870 0 0.5240 5.6310 100.00 6.0821 5 311.0 15.20 386.63 29.93 16.50
	0.17004 12.50 7.870 0 0.5240 6.0040 85.90 6.5921 5 311.0 15.20 386.71 17.10 18.90
	import tensorflow as tf

	policy = tf.keras.mixed_precision.Policy('mixed_float16')
	tf.keras.mixed_precision.set_global_policy(policy)

	inputs = keras.Input(shape=(784,))
	x = tf.keras.layers.Dense(4096, activation='relu')(inputs)
	x = tf.keras.layers.Dense(4096, activation='relu')(x)
	x = layers.Dense(10)(x)
	outputs = layers.Activation('softmax', dtype='float32')(x)
	import kerastuner as kt
	import tensorflow as tf

	def model_builder(hp):
	model = tf.keras.Sequential()
	model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))

	# Tune the number of units in the first Dense layer
	# Choose an optimal value between 32-512
	hp_units = hp.Int('units', min_value = 32, max_value = 512, step = 32)
	def model_builder(hp):
	model = keras.Sequential()
	model.add(keras.layers.Flatten(input_shape=(28, 28)))

	# Tune the number of units in the first Dense layer
	# Choose an optimal value between 32-512
	hp_units = hp.Int('units', min_value = 32, max_value = 512, step = 32)
	model.add(keras.layers.Dense(units = hp_units, activation = 'relu'))
	model.add(keras.layers.Dense(10))