Rishit-dagli

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Rishit-dagli

Rishit-dagli

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

Rishit-dagli

Rishit-dagli

 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

Rishit-dagli

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)

Rishit-dagli

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)

Rishit-dagli

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))

Rishit-dagli

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)

Rishit-dagli

initial_learning_rate = 0.1
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate,
    decay_steps = 100000,
    decay_rate = 0.96,
    staircase = True)

model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate = lr_schedule),
              loss = 'sparse_categorical_crossentropy',
              metrics = ['accuracy'])