This notebook contains implementation of some tensorflow basics

INITIAL IMPORTS

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf

TENSOR BASICS

x = tf.constant(4, shape=(1,1), dtype=float)

x

<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[4.]], dtype=float32)>

x = tf.constant([[1,2,3], [4,5,6]])

x

<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)>

x = tf.ones((4,4))
x

<tf.Tensor: shape=(4, 4), dtype=float32, numpy=
array([[1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.]], dtype=float32)>

x = tf.zeros((3,3))
x

<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]], dtype=float32)>

x = tf.eye((3))

x

<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]], dtype=float32)>

x = tf.random.normal((3,3), mean=0, stddev=1)
x

<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 0.07876126, -1.7608395 ,  2.0851367 ],
       [ 0.03440803, -0.14894831, -0.34667596],
       [ 0.04498795,  1.1451246 ,  0.739446  ]], dtype=float32)>

x = tf.random.uniform((1,3), minval=0, maxval=1)
x

<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[0.70544374, 0.9578012 , 0.9191464 ]], dtype=float32)>

x = tf.range(9)
x

<tf.Tensor: shape=(9,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int32)>

x = tf.range(start=1, limit=20, delta=3)
x

<tf.Tensor: shape=(7,), dtype=int32, numpy=array([ 1,  4,  7, 10, 13, 16, 19], dtype=int32)>

x = tf.constant([1,2,3])
y = tf.constant([7,8,9])
z = tf.add(x,y)
z

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([ 8, 10, 12], dtype=int32)>

x = tf.constant([1,2,3])
y = tf.constant([7,8,9])
z = tf.subtract(x,y)
z

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([-6, -6, -6], dtype=int32)>

x = tf.constant([1,2,3])
y = tf.constant([7,8,9])
z = tf.multiply(x,y)
z

<tf.Tensor: shape=(3,), dtype=int32, numpy=array([ 7, 16, 27], dtype=int32)>

x = tf.constant([1,2,3])
y = tf.constant([7,8,9])
z = tf.tensordot(x,y, axes=1)
z

<tf.Tensor: shape=(), dtype=int32, numpy=50>

x = tf.constant([1,2,3])
y = tf.constant([7,8,9])
z = tf.reduce_sum(x*y, axis=0)
z

<tf.Tensor: shape=(), dtype=int32, numpy=50>

x = tf.random.normal((2,3))
y = tf.random.normal((3,4))
z = tf.matmul(x,y)
z

<tf.Tensor: shape=(2, 4), dtype=float32, numpy=
array([[-0.50085664, -0.94550675,  0.42043906, -1.3031094 ],
       [ 0.30706838,  1.6291472 ,  0.32749945, -2.265583  ]],
      dtype=float32)>

x = tf.constant([[1,2],
                 [3,4],
                 [5,6]])
x[0,1]

<tf.Tensor: shape=(), dtype=int32, numpy=2>

x = tf.range(9)
x = tf.reshape(x, (3,3))
x = tf.transpose(x, perm=[1,0])
x

<tf.Tensor: shape=(3, 3), dtype=int32, numpy=
array([[0, 3, 6],
       [1, 4, 7],
       [2, 5, 8]], dtype=int32)>

DESIGN BASIC NN USING SEQUENTIAL API

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
print(x_train.shape)

x_train = x_train.reshape(-1, 28*28).astype('float32') 
x_test = x_test.reshape(-1, 28*28).astype('float32') 

(60000, 28, 28)

model = keras.Sequential(
    [
     keras.Input(shape=(28*28)),
     layers.Dense(512, activation='relu'),
     layers.Dense(256, activation='relu'),
     layers.Dense(10),
    ]

)
print(model.summary())
model.compile(
    
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ['accuracy'],
)

model.fit(x_train, y_train, batch_size=32, epochs=5, verbose=2)
model.evaluate(x_test, y_test, batch_size=32, verbose=2)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 512)               401920    
_________________________________________________________________
dense_7 (Dense)              (None, 256)               131328    
_________________________________________________________________
dense_8 (Dense)              (None, 10)                2570      
=================================================================
Total params: 535,818
Trainable params: 535,818
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
1875/1875 - 2s - loss: 0.1857 - accuracy: 0.9431
Epoch 2/5
1875/1875 - 2s - loss: 0.0778 - accuracy: 0.9757
Epoch 3/5
1875/1875 - 2s - loss: 0.0550 - accuracy: 0.9827
Epoch 4/5
1875/1875 - 2s - loss: 0.0411 - accuracy: 0.9867
Epoch 5/5
1875/1875 - 2s - loss: 0.0321 - accuracy: 0.9899
313/313 - 0s - loss: 0.0863 - accuracy: 0.9786

[0.08630800992250443, 0.978600025177002]

model = keras.Sequential()
model.add(keras.Input(shape=(784))),
model.add(layers.Dense(512, activation='relu', name='first_layer')),
model.add(layers.Dense(256, activation='relu')),
model.add(layers.Dense(128, activation='relu')),
model.add(layers.Dense(64, activation='relu')),
model.add(layers.Dense(10))


print(model.summary())
model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ['accuracy'],
)

model.fit(x_train, y_train, batch_size=32, epochs=8, verbose=2)

model.evaluate(x_test, y_test, batch_size=32, verbose=2)

Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
first_layer (Dense)          (None, 512)               401920    
_________________________________________________________________
dense_19 (Dense)             (None, 256)               131328    
_________________________________________________________________
dense_20 (Dense)             (None, 128)               32896     
_________________________________________________________________
dense_21 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_22 (Dense)             (None, 10)                650       
=================================================================
Total params: 575,050
Trainable params: 575,050
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/8
1875/1875 - 3s - loss: 0.6634 - accuracy: 0.8987
Epoch 2/8
1875/1875 - 3s - loss: 0.1697 - accuracy: 0.9525
Epoch 3/8
1875/1875 - 3s - loss: 0.1338 - accuracy: 0.9616
Epoch 4/8
1875/1875 - 3s - loss: 0.1220 - accuracy: 0.9660
Epoch 5/8
1875/1875 - 2s - loss: 0.1027 - accuracy: 0.9716
Epoch 6/8
1875/1875 - 3s - loss: 0.0847 - accuracy: 0.9765
Epoch 7/8
1875/1875 - 3s - loss: 0.0818 - accuracy: 0.9783
Epoch 8/8
1875/1875 - 3s - loss: 0.0754 - accuracy: 0.9804
313/313 - 0s - loss: 0.1218 - accuracy: 0.9696

[0.12180296331644058, 0.9696000218391418]

CNN

from tensorflow import keras
from tensorflow.keras import layers, regularizers
from tensorflow.keras.datasets import cifar10

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 2s 0us/step

model = keras.Sequential([
    keras.Input(shape=(32,32,3)),
    layers.Conv2D(32, 3, activation='relu'),
    layers.MaxPooling2D(pool_size=(2,2)),
    layers.Conv2D(64, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(128, 3, activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10),
])

model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=3e-4),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=15, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

Epoch 1/15
782/782 - 9s - loss: 1.7280 - accuracy: 0.3719
Epoch 2/15
782/782 - 2s - loss: 1.3791 - accuracy: 0.5054
Epoch 3/15
782/782 - 2s - loss: 1.2507 - accuracy: 0.5575
Epoch 4/15
782/782 - 2s - loss: 1.1617 - accuracy: 0.5932
Epoch 5/15
782/782 - 2s - loss: 1.0858 - accuracy: 0.6187
Epoch 6/15
782/782 - 2s - loss: 1.0311 - accuracy: 0.6411
Epoch 7/15
782/782 - 2s - loss: 0.9734 - accuracy: 0.6607
Epoch 8/15
782/782 - 2s - loss: 0.9331 - accuracy: 0.6761
Epoch 9/15
782/782 - 2s - loss: 0.8953 - accuracy: 0.6893
Epoch 10/15
782/782 - 2s - loss: 0.8547 - accuracy: 0.7039
Epoch 11/15
782/782 - 2s - loss: 0.8226 - accuracy: 0.7163
Epoch 12/15
782/782 - 2s - loss: 0.7896 - accuracy: 0.7264
Epoch 13/15
782/782 - 2s - loss: 0.7551 - accuracy: 0.7377
Epoch 14/15
782/782 - 2s - loss: 0.7273 - accuracy: 0.7482
Epoch 15/15
782/782 - 2s - loss: 0.7002 - accuracy: 0.7559
157/157 - 0s - loss: 0.8706 - accuracy: 0.6971

[0.8705907464027405, 0.6970999836921692]

FUNCTIONAL API

def my_model():
    inputs = keras.Input(shape=(32,32,3))
    x = layers.Conv2D(32, 3, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(64, 3, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(128, 3, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.Flatten()(x)
    x = layers.Dense(64, activation='relu')(x)    
    outputs = layers.Dense(10)(x)
    model = keras.Model(inputs = inputs, outputs = outputs) 
    return model

model = my_model()

model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=3e-4),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=15, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)   

REGULARIZATION FOR OVERFITTING DATA

def my_model():
    inputs = keras.Input(shape=(32,32,3))
    x = layers.Conv2D(
        32, 3, padding='same', kernel_regularizer=regularizers.l2(0.01),
                      )(inputs)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(
        64, 3, padding='same', kernel_regularizer=regularizers.l2(0.01),
        )(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(
        128, 3, padding='same', kernel_regularizer=regularizers.l2(0.01),
        )(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.Flatten()(x)
    x = layers.Dense(
        64, activation='relu', kernel_regularizer=regularizers.l2(0.01),
        )(x)
    x = layers.Dropout(0.5)(x)    
    outputs = layers.Dense(10)(x)
    model = keras.Model(inputs = inputs, outputs = outputs) 
    return model

model = my_model()

model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=3e-4),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=150, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)   

Epoch 1/150
782/782 - 3s - loss: 3.0665 - accuracy: 0.3407
Epoch 2/150
782/782 - 3s - loss: 1.9365 - accuracy: 0.4563
Epoch 3/150
782/782 - 3s - loss: 1.6477 - accuracy: 0.5120
Epoch 4/150
782/782 - 3s - loss: 1.5146 - accuracy: 0.5401
Epoch 5/150
782/782 - 3s - loss: 1.4456 - accuracy: 0.5563
Epoch 6/150
782/782 - 3s - loss: 1.4056 - accuracy: 0.5704
Epoch 7/150
782/782 - 3s - loss: 1.3693 - accuracy: 0.5821
Epoch 8/150
782/782 - 3s - loss: 1.3452 - accuracy: 0.5895
Epoch 9/150
782/782 - 3s - loss: 1.3283 - accuracy: 0.5981
Epoch 10/150
782/782 - 3s - loss: 1.3048 - accuracy: 0.6054
Epoch 11/150
782/782 - 3s - loss: 1.2896 - accuracy: 0.6124
Epoch 12/150
782/782 - 3s - loss: 1.2815 - accuracy: 0.6147
Epoch 13/150
782/782 - 3s - loss: 1.2636 - accuracy: 0.6246
Epoch 14/150
782/782 - 3s - loss: 1.2525 - accuracy: 0.6318
Epoch 15/150
782/782 - 3s - loss: 1.2353 - accuracy: 0.6395
Epoch 16/150
782/782 - 3s - loss: 1.2233 - accuracy: 0.6443
Epoch 17/150
782/782 - 3s - loss: 1.2178 - accuracy: 0.6477
Epoch 18/150
782/782 - 3s - loss: 1.2123 - accuracy: 0.6537
Epoch 19/150
782/782 - 3s - loss: 1.1972 - accuracy: 0.6605
Epoch 20/150
782/782 - 3s - loss: 1.1886 - accuracy: 0.6649
Epoch 21/150
782/782 - 3s - loss: 1.1833 - accuracy: 0.6639
Epoch 22/150
782/782 - 3s - loss: 1.1700 - accuracy: 0.6718
Epoch 23/150
782/782 - 3s - loss: 1.1634 - accuracy: 0.6742
Epoch 24/150
782/782 - 3s - loss: 1.1603 - accuracy: 0.6763
Epoch 25/150
782/782 - 3s - loss: 1.1456 - accuracy: 0.6814
Epoch 26/150
782/782 - 3s - loss: 1.1423 - accuracy: 0.6841
Epoch 27/150
782/782 - 3s - loss: 1.1301 - accuracy: 0.6870
Epoch 28/150
782/782 - 3s - loss: 1.1283 - accuracy: 0.6911
Epoch 29/150
782/782 - 3s - loss: 1.1164 - accuracy: 0.6952
Epoch 30/150
782/782 - 3s - loss: 1.1120 - accuracy: 0.6987
Epoch 31/150
782/782 - 3s - loss: 1.1038 - accuracy: 0.7028
Epoch 32/150
782/782 - 3s - loss: 1.1046 - accuracy: 0.7032
Epoch 33/150
782/782 - 3s - loss: 1.0965 - accuracy: 0.7057
Epoch 34/150
782/782 - 3s - loss: 1.0919 - accuracy: 0.7089
Epoch 35/150
782/782 - 3s - loss: 1.0816 - accuracy: 0.7111
Epoch 36/150
782/782 - 3s - loss: 1.0762 - accuracy: 0.7148
Epoch 37/150
782/782 - 3s - loss: 1.0763 - accuracy: 0.7157
Epoch 38/150
782/782 - 3s - loss: 1.0689 - accuracy: 0.7176
Epoch 39/150
782/782 - 3s - loss: 1.0638 - accuracy: 0.7218
Epoch 40/150
782/782 - 3s - loss: 1.0591 - accuracy: 0.7231
Epoch 41/150
782/782 - 3s - loss: 1.0605 - accuracy: 0.7217
Epoch 42/150
782/782 - 3s - loss: 1.0586 - accuracy: 0.7263
Epoch 43/150
782/782 - 3s - loss: 1.0457 - accuracy: 0.7285
Epoch 44/150
782/782 - 3s - loss: 1.0492 - accuracy: 0.7310
Epoch 45/150
782/782 - 3s - loss: 1.0466 - accuracy: 0.7316
Epoch 46/150
782/782 - 3s - loss: 1.0325 - accuracy: 0.7354
Epoch 47/150
782/782 - 3s - loss: 1.0347 - accuracy: 0.7354
Epoch 48/150
782/782 - 3s - loss: 1.0329 - accuracy: 0.7339
Epoch 49/150
782/782 - 3s - loss: 1.0339 - accuracy: 0.7347
Epoch 50/150
782/782 - 3s - loss: 1.0182 - accuracy: 0.7416
Epoch 51/150
782/782 - 3s - loss: 1.0294 - accuracy: 0.7409
Epoch 52/150
782/782 - 3s - loss: 1.0151 - accuracy: 0.7441
Epoch 53/150
782/782 - 3s - loss: 1.0199 - accuracy: 0.7437
Epoch 54/150
782/782 - 3s - loss: 1.0240 - accuracy: 0.7434
Epoch 55/150
782/782 - 3s - loss: 1.0226 - accuracy: 0.7448
Epoch 56/150
782/782 - 3s - loss: 1.0059 - accuracy: 0.7464
Epoch 57/150
782/782 - 3s - loss: 1.0077 - accuracy: 0.7474
Epoch 58/150
782/782 - 3s - loss: 1.0038 - accuracy: 0.7494
Epoch 59/150
782/782 - 3s - loss: 1.0003 - accuracy: 0.7539
Epoch 60/150
782/782 - 3s - loss: 1.0004 - accuracy: 0.7509
Epoch 61/150
782/782 - 3s - loss: 0.9958 - accuracy: 0.7517
Epoch 62/150
782/782 - 3s - loss: 0.9954 - accuracy: 0.7548
Epoch 63/150
782/782 - 3s - loss: 0.9892 - accuracy: 0.7567
Epoch 64/150
782/782 - 3s - loss: 0.9899 - accuracy: 0.7570
Epoch 65/150
782/782 - 3s - loss: 0.9946 - accuracy: 0.7554
Epoch 66/150
782/782 - 3s - loss: 0.9888 - accuracy: 0.7554
Epoch 67/150
782/782 - 3s - loss: 0.9824 - accuracy: 0.7593
Epoch 68/150
782/782 - 3s - loss: 0.9819 - accuracy: 0.7591
Epoch 69/150
782/782 - 3s - loss: 0.9878 - accuracy: 0.7575
Epoch 70/150
782/782 - 3s - loss: 0.9794 - accuracy: 0.7620
Epoch 71/150
782/782 - 3s - loss: 0.9759 - accuracy: 0.7629
Epoch 72/150
782/782 - 3s - loss: 0.9744 - accuracy: 0.7638
Epoch 73/150
782/782 - 3s - loss: 0.9626 - accuracy: 0.7672
Epoch 74/150
782/782 - 3s - loss: 0.9781 - accuracy: 0.7636
Epoch 75/150
782/782 - 3s - loss: 0.9743 - accuracy: 0.7647
Epoch 76/150
782/782 - 3s - loss: 0.9701 - accuracy: 0.7674
Epoch 77/150
782/782 - 3s - loss: 0.9642 - accuracy: 0.7701
Epoch 78/150
782/782 - 3s - loss: 0.9645 - accuracy: 0.7701
Epoch 79/150
782/782 - 3s - loss: 0.9605 - accuracy: 0.7682
Epoch 80/150
782/782 - 3s - loss: 0.9729 - accuracy: 0.7678
Epoch 81/150
782/782 - 3s - loss: 0.9605 - accuracy: 0.7713
Epoch 82/150
782/782 - 3s - loss: 0.9617 - accuracy: 0.7704
Epoch 83/150
782/782 - 3s - loss: 0.9552 - accuracy: 0.7728
Epoch 84/150
782/782 - 3s - loss: 0.9518 - accuracy: 0.7726
Epoch 85/150
782/782 - 3s - loss: 0.9508 - accuracy: 0.7758
Epoch 86/150
782/782 - 3s - loss: 0.9555 - accuracy: 0.7741
Epoch 87/150
782/782 - 3s - loss: 0.9475 - accuracy: 0.7748
Epoch 88/150
782/782 - 3s - loss: 0.9526 - accuracy: 0.7741
Epoch 89/150
782/782 - 3s - loss: 0.9512 - accuracy: 0.7777
Epoch 90/150
782/782 - 3s - loss: 0.9522 - accuracy: 0.7757
Epoch 91/150
782/782 - 3s - loss: 0.9506 - accuracy: 0.7771
Epoch 92/150
782/782 - 3s - loss: 0.9446 - accuracy: 0.7773
Epoch 93/150
782/782 - 3s - loss: 0.9485 - accuracy: 0.7746
Epoch 94/150
782/782 - 3s - loss: 0.9511 - accuracy: 0.7737
Epoch 95/150
782/782 - 3s - loss: 0.9449 - accuracy: 0.7766
Epoch 96/150
782/782 - 3s - loss: 0.9449 - accuracy: 0.7786
Epoch 97/150
782/782 - 3s - loss: 0.9468 - accuracy: 0.7783
Epoch 98/150
782/782 - 3s - loss: 0.9415 - accuracy: 0.7810
Epoch 99/150
782/782 - 3s - loss: 0.9389 - accuracy: 0.7783
Epoch 100/150
782/782 - 3s - loss: 0.9403 - accuracy: 0.7816
Epoch 101/150
782/782 - 3s - loss: 0.9364 - accuracy: 0.7818
Epoch 102/150
782/782 - 3s - loss: 0.9300 - accuracy: 0.7850
Epoch 103/150
782/782 - 3s - loss: 0.9326 - accuracy: 0.7837
Epoch 104/150
782/782 - 3s - loss: 0.9323 - accuracy: 0.7831
Epoch 105/150
782/782 - 3s - loss: 0.9434 - accuracy: 0.7793
Epoch 106/150
782/782 - 3s - loss: 0.9386 - accuracy: 0.7822
Epoch 107/150
782/782 - 3s - loss: 0.9285 - accuracy: 0.7854
Epoch 108/150
782/782 - 3s - loss: 0.9324 - accuracy: 0.7842
Epoch 109/150
782/782 - 3s - loss: 0.9391 - accuracy: 0.7829
Epoch 110/150
782/782 - 3s - loss: 0.9310 - accuracy: 0.7853
Epoch 111/150
782/782 - 3s - loss: 0.9311 - accuracy: 0.7843
Epoch 112/150
782/782 - 3s - loss: 0.9338 - accuracy: 0.7852
Epoch 113/150
782/782 - 3s - loss: 0.9265 - accuracy: 0.7859
Epoch 114/150
782/782 - 3s - loss: 0.9264 - accuracy: 0.7859
Epoch 115/150
782/782 - 3s - loss: 0.9281 - accuracy: 0.7844
Epoch 116/150
782/782 - 3s - loss: 0.9313 - accuracy: 0.7849
Epoch 117/150
782/782 - 3s - loss: 0.9263 - accuracy: 0.7887
Epoch 118/150
782/782 - 3s - loss: 0.9325 - accuracy: 0.7855
Epoch 119/150
782/782 - 3s - loss: 0.9271 - accuracy: 0.7869
Epoch 120/150
782/782 - 3s - loss: 0.9141 - accuracy: 0.7921
Epoch 121/150
782/782 - 3s - loss: 0.9306 - accuracy: 0.7861
Epoch 122/150
782/782 - 3s - loss: 0.9221 - accuracy: 0.7907
Epoch 123/150
782/782 - 3s - loss: 0.9207 - accuracy: 0.7882
Epoch 124/150
782/782 - 3s - loss: 0.9203 - accuracy: 0.7920
Epoch 125/150
782/782 - 3s - loss: 0.9231 - accuracy: 0.7866
Epoch 126/150
782/782 - 3s - loss: 0.9227 - accuracy: 0.7899
Epoch 127/150
782/782 - 3s - loss: 0.9191 - accuracy: 0.7901
Epoch 128/150
782/782 - 3s - loss: 0.9218 - accuracy: 0.7903
Epoch 129/150
782/782 - 3s - loss: 0.9217 - accuracy: 0.7916
Epoch 130/150
782/782 - 3s - loss: 0.9240 - accuracy: 0.7890
Epoch 131/150
782/782 - 3s - loss: 0.9242 - accuracy: 0.7900
Epoch 132/150
782/782 - 3s - loss: 0.9202 - accuracy: 0.7903
Epoch 133/150
782/782 - 3s - loss: 0.9073 - accuracy: 0.7940
Epoch 134/150
782/782 - 3s - loss: 0.9142 - accuracy: 0.7928
Epoch 135/150
782/782 - 3s - loss: 0.9181 - accuracy: 0.7916
Epoch 136/150
782/782 - 3s - loss: 0.9152 - accuracy: 0.7942
Epoch 137/150
782/782 - 3s - loss: 0.9139 - accuracy: 0.7928
Epoch 138/150
782/782 - 3s - loss: 0.9211 - accuracy: 0.7896
Epoch 139/150
782/782 - 3s - loss: 0.9198 - accuracy: 0.7915
Epoch 140/150
782/782 - 3s - loss: 0.9092 - accuracy: 0.7943
Epoch 141/150
782/782 - 3s - loss: 0.9098 - accuracy: 0.7969
Epoch 142/150
782/782 - 3s - loss: 0.9147 - accuracy: 0.7923
Epoch 143/150
782/782 - 3s - loss: 0.9105 - accuracy: 0.7953
Epoch 144/150
782/782 - 3s - loss: 0.9214 - accuracy: 0.7926
Epoch 145/150
782/782 - 3s - loss: 0.9097 - accuracy: 0.7969
Epoch 146/150
782/782 - 3s - loss: 0.9135 - accuracy: 0.7970
Epoch 147/150
782/782 - 3s - loss: 0.9079 - accuracy: 0.7949
Epoch 148/150
782/782 - 3s - loss: 0.9120 - accuracy: 0.7965
Epoch 149/150
782/782 - 3s - loss: 0.9130 - accuracy: 0.7963
Epoch 150/150
782/782 - 3s - loss: 0.9114 - accuracy: 0.7938
157/157 - 1s - loss: 1.0472 - accuracy: 0.7640

[1.0472173690795898, 0.7639999985694885]

RNN, GRU, LSTM

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
print(x_train.shape)

x_train = x_train.astype('float32') 
x_test = x_test.astype('float32') 

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
(60000, 28, 28)

model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.SimpleRNN(512, return_sequences=True, activation='relu'))
model.add(layers.SimpleRNN(512, activation='relu'))
model.add(layers.Dense(10))

print(model.summary())
model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
simple_rnn (SimpleRNN)       (None, None, 512)         276992    
_________________________________________________________________
simple_rnn_1 (SimpleRNN)     (None, 512)               524800    
_________________________________________________________________
dense (Dense)                (None, 10)                5130      
=================================================================
Total params: 806,922
Trainable params: 806,922
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/10
938/938 - 34s - loss: 1.0213 - accuracy: 0.6945
Epoch 2/10
938/938 - 32s - loss: 0.3255 - accuracy: 0.8994
Epoch 3/10
938/938 - 31s - loss: 0.2171 - accuracy: 0.9409
Epoch 4/10
938/938 - 31s - loss: 0.1731 - accuracy: 0.9532
Epoch 5/10
938/938 - 32s - loss: 0.1591 - accuracy: 0.9595
Epoch 6/10
938/938 - 32s - loss: 0.1528 - accuracy: 0.9614
Epoch 7/10
938/938 - 32s - loss: 0.1423 - accuracy: 0.9646
Epoch 8/10
938/938 - 32s - loss: 0.1486 - accuracy: 0.9633
Epoch 9/10
938/938 - 31s - loss: 0.1367 - accuracy: 0.9660
Epoch 10/10
938/938 - 31s - loss: 0.1134 - accuracy: 0.9717
157/157 - 1s - loss: 0.1057 - accuracy: 0.9759

[0.10572042316198349, 0.9758999943733215]

GRUs

model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.GRU(256, return_sequences=True, activation='relu'))
model.add(layers.GRU(256, activation='relu'))
model.add(layers.Dense(10))

print(model.summary())
model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

WARNING:tensorflow:Layer gru will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer gru_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
gru (GRU)                    (None, None, 256)         219648    
_________________________________________________________________
gru_1 (GRU)                  (None, 256)               394752    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 616,970
Trainable params: 616,970
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/10
938/938 - 91s - loss: 0.5875 - accuracy: 0.8393
Epoch 2/10
938/938 - 90s - loss: 0.2172 - accuracy: 0.9316
Epoch 3/10
938/938 - 90s - loss: 0.1624 - accuracy: 0.9496
Epoch 4/10
938/938 - 89s - loss: 0.1528 - accuracy: 0.9539
Epoch 5/10
938/938 - 89s - loss: 0.1400 - accuracy: 0.9579
Epoch 6/10
938/938 - 90s - loss: 0.1437 - accuracy: 0.9562
Epoch 7/10
938/938 - 91s - loss: 0.1323 - accuracy: 0.9609
Epoch 8/10
938/938 - 89s - loss: 0.1070 - accuracy: 0.9686
Epoch 9/10
938/938 - 91s - loss: 0.1500 - accuracy: 0.9560
Epoch 10/10
938/938 - 89s - loss: 0.1155 - accuracy: 0.9661
157/157 - 2s - loss: 0.1240 - accuracy: 0.9635

[0.12400849908590317, 0.9635000228881836]

LSTM

model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.LSTM(256, return_sequences=True, activation='relu'))
model.add(layers.LSTM(256, activation='relu'))
model.add(layers.Dense(10))

print(model.summary())
model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, None, 256)         291840    
_________________________________________________________________
lstm_1 (LSTM)                (None, 256)               525312    
_________________________________________________________________
dense_2 (Dense)              (None, 10)                2570      
=================================================================
Total params: 819,722
Trainable params: 819,722
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/10
938/938 - 76s - loss: 772.9935 - accuracy: 0.1622
Epoch 2/10
938/938 - 74s - loss: 176.0788 - accuracy: 0.3707
Epoch 3/10
938/938 - 75s - loss: 34.8663 - accuracy: 0.5478
Epoch 4/10
938/938 - 75s - loss: 25.1809 - accuracy: 0.6143
Epoch 5/10
938/938 - 76s - loss: 28.4628 - accuracy: 0.5853
Epoch 6/10
938/938 - 76s - loss: 15.4300 - accuracy: 0.6723
Epoch 7/10
938/938 - 76s - loss: 10.9364 - accuracy: 0.7126
Epoch 8/10
938/938 - 76s - loss: 8.5499 - accuracy: 0.7437
Epoch 9/10
938/938 - 75s - loss: 6.2875 - accuracy: 0.7729
Epoch 10/10
938/938 - 76s - loss: 5.1225 - accuracy: 0.7874
157/157 - 1s - loss: 6.2078 - accuracy: 0.7416

[6.207762718200684, 0.741599977016449]

BIDIRECTIONAL LSTM

model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.Bidirectional(layers.LSTM(256, return_sequences=True, activation='relu')))
model.add(layers.Bidirectional(layers.LSTM(256, activation='relu')))
model.add(layers.Dense(10))

print(model.summary())
model.compile(
    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer = keras.optimizers.Adam(learning_rate=0.001),
    metrics = ["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bidirectional (Bidirectional (None, None, 512)         583680    
_________________________________________________________________
bidirectional_1 (Bidirection (None, 512)               1574912   
_________________________________________________________________
dense (Dense)                (None, 10)                5130      
=================================================================
Total params: 2,163,722
Trainable params: 2,163,722
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/10
938/938 - 166s - loss: 8292.4199 - accuracy: 0.1552
Epoch 2/10
Epoch 3/10
938/938 - 163s - loss: 3408.4773 - accuracy: 0.1964
Epoch 4/10
938/938 - 162s - loss: 4644.5352 - accuracy: 0.1910
Epoch 5/10

ADDITIONAL EXAMPLE OF FUNCTIONAL API ON MULTI DIGIT MNIST DATASET

!unzip /content/drive/MyDrive/TFBASICS/MDMNIST.zip

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import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, regularizers
from tensorflow.keras.datasets import mnist

# Use Pandas to load dataset from csv file
import pandas as pd

# HYPERPARAMETERS
BATCH_SIZE = 64
WEIGHT_DECAY = 0.001
LEARNING_RATE = 0.001

# Make sure we don't get any GPU errors, no need if you are using Google Colab
physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

train_df = pd.read_csv("/content/train.csv")
test_df = pd.read_csv("/content/test.csv")
train_images = "/content/train_images/" + train_df.iloc[:, 0].values
test_images = "/content/test_images/" + test_df.iloc[:, 0].values

train_labels = train_df.iloc[:, 1:].values
test_labels = test_df.iloc[:, 1:].values


def read_image(image_path, label):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_image(image, channels=1, dtype=tf.float32)

    # In older versions you need to set shape in order to avoid error
    # on newer (2.3.0+) the following 3 lines can safely be removed
    image.set_shape((64, 64, 1))
    label[0].set_shape([])
    label[1].set_shape([])

    labels = {"first_num": label[0], "second_num": label[1]}
    return image, labels


AUTOTUNE = tf.data.experimental.AUTOTUNE
train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
train_dataset = (
    train_dataset.shuffle(buffer_size=len(train_labels))
    .map(read_image)
    .batch(batch_size=BATCH_SIZE)
    .prefetch(buffer_size=AUTOTUNE)
)

test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels))
test_dataset = (
    test_dataset.map(read_image)
    .batch(batch_size=BATCH_SIZE)
    .prefetch(buffer_size=AUTOTUNE)
)

inputs = keras.Input(shape=(64, 64, 1))
x = layers.Conv2D(
    filters=32,
    kernel_size=3,
    padding="same",
    kernel_regularizer=regularizers.l2(WEIGHT_DECAY),
)(inputs)
x = layers.BatchNormalization()(x)
x = keras.activations.relu(x)
x = layers.Conv2D(64, 3, kernel_regularizer=regularizers.l2(WEIGHT_DECAY),)(x)
x = layers.BatchNormalization()(x)
x = keras.activations.relu(x)
x = layers.MaxPooling2D()(x)
x = layers.Conv2D(
    64, 3, activation="relu", kernel_regularizer=regularizers.l2(WEIGHT_DECAY),
)(x)
x = layers.Conv2D(128, 3, activation="relu")(x)
x = layers.MaxPooling2D()(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation="relu")(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(64, activation="relu")(x)
output1 = layers.Dense(10, activation="softmax", name="first_num")(x)
output2 = layers.Dense(10, activation="softmax", name="second_num")(x)
model = keras.Model(inputs=inputs, outputs=[output1, output2])

model.compile(
    optimizer=keras.optimizers.Adam(LEARNING_RATE),
    loss=keras.losses.SparseCategoricalCrossentropy(),
    metrics=["accuracy"],
)

model.fit(train_dataset, epochs=5, verbose=2)
model.evaluate(test_dataset, verbose=2)

Epoch 1/5
1000/1000 - 33s - loss: 1.7513 - first_num_loss: 0.8472 - second_num_loss: 0.8236 - first_num_accuracy: 0.7005 - second_num_accuracy: 0.7139
Epoch 2/5
1000/1000 - 27s - loss: 0.5215 - first_num_loss: 0.2282 - second_num_loss: 0.2246 - first_num_accuracy: 0.9253 - second_num_accuracy: 0.9282
Epoch 3/5
1000/1000 - 28s - loss: 0.3395 - first_num_loss: 0.1399 - second_num_loss: 0.1377 - first_num_accuracy: 0.9556 - second_num_accuracy: 0.9564
Epoch 4/5
1000/1000 - 28s - loss: 0.2717 - first_num_loss: 0.1091 - second_num_loss: 0.1050 - first_num_accuracy: 0.9650 - second_num_accuracy: 0.9666
Epoch 5/5
1000/1000 - 27s - loss: 0.2349 - first_num_loss: 0.0932 - second_num_loss: 0.0872 - first_num_accuracy: 0.9704 - second_num_accuracy: 0.9725
313/313 - 6s - loss: 0.9557 - first_num_loss: 0.2400 - second_num_loss: 0.6635 - first_num_accuracy: 0.9284 - second_num_accuracy: 0.8163

[0.9557247161865234,
 0.23999570310115814,
 0.66352379322052,
 0.9283999800682068,
 0.8162500262260437]

MODEL SUBCLASSING WITH KERAS

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0

# CNN -> BatchNorm -> ReLU (common structure)
# x10 (a lot of code to write!)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step

class CNNBlock(layers.Layer):
    def __init__(self, out_channels, kernel_size=3):
        super(CNNBlock, self).__init__()
        self.conv = layers.Conv2D(out_channels, kernel_size, padding="same")
        self.bn = layers.BatchNormalization()

    def call(self, input_tensor, training=False):
        x = self.conv(input_tensor)
        x = self.bn(x, training=training)
        x = tf.nn.relu(x)
        return x

# model = keras.Sequential(
#     [CNNBlock(32), CNNBlock(64), CNNBlock(128), layers.Flatten(), layers.Dense(10),]
# )
# model.compile(
#     optimizer=keras.optimizers.Adam(lr=0.001),
#     loss=keras.losses.SparseCategoricalCrossentropy(),
#     metrics=["accuracy"],
# )

# model.fit(x_train, y_train, batch_size=64, epochs=2, verbose =2)
# print(model.summary())

Epoch 1/2
938/938 - 17s - loss: 2.5179 - accuracy: 0.1896
Epoch 2/2
938/938 - 10s - loss: 2.2146 - accuracy: 0.1937
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
cnn_block_9 (CNNBlock)       (None, 28, 28, 32)        448       
_________________________________________________________________
cnn_block_10 (CNNBlock)      (None, 28, 28, 64)        18752     
_________________________________________________________________
cnn_block_11 (CNNBlock)      (None, 28, 28, 128)       74368     
_________________________________________________________________
flatten_3 (Flatten)          (None, 100352)            0         
_________________________________________________________________
dense_3 (Dense)              (None, 10)                1003530   
=================================================================
Total params: 1,097,098
Trainable params: 1,096,650
Non-trainable params: 448
_________________________________________________________________
None

class ResBlock(layers.Layer):
    def __init__(self, channels):
        super(ResBlock, self).__init__()
        self.channels = channels
        self.cnn1 = CNNBlock(channels[0], 3)
        self.cnn2 = CNNBlock(channels[1], 3)
        self.cnn3 = CNNBlock(channels[2], 3)
        self.pooling = layers.MaxPooling2D()
        self.identity_mapping = layers.Conv2D(channels[1], 3, padding="same")

    def call(self, input_tensor, training=False):
        x = self.cnn1(input_tensor, training=training)
        x = self.cnn2(x, training=training)
        x = self.cnn3(x + self.identity_mapping(input_tensor), training=training,)
        x = self.pooling(x)
        return x

class ResNet_Like(keras.Model):
    def __init__(self, num_classes=10):
        super(ResNet_Like, self).__init__()
        self.block1 = ResBlock([32, 32, 64])
        self.block2 = ResBlock([128, 128, 256])
        self.block3 = ResBlock([128, 256, 512])
        self.pool = layers.Flatten()
        self.classifier = layers.Dense(num_classes)

    def call(self, input_tensor, training=False):
        x = self.block1(input_tensor, training=training)
        x = self.block2(x, training=training)
        x = self.block3(x, training=training)
        x = self.pool(x, training=training)
        x = self.classifier(x)
        return x

    def model(self):
        x = keras.Input(shape=(28, 28, 1))
        return keras.Model(inputs=[x], outputs=self.call(x))

# we can use layers.Flatten() instead of layers.GlobalAveragePooling2D()

model = ResNet_Like().model()
base_input = model.layers[0].input
base_output = model.layers[2].output
output = layers.Dense(10)(layers.Flatten()(base_output))
model = keras.Model(base_input, output)

model.compile(
    optimizer=keras.optimizers.Adam(),
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=20, verbose=2)
print(model.summary())
model.evaluate(x_test, y_test, batch_size=64, verbose=2)

Epoch 1/20
938/938 - 17s - loss: 0.1054 - accuracy: 0.9672
Epoch 2/20
938/938 - 16s - loss: 0.0406 - accuracy: 0.9877
Epoch 3/20
938/938 - 16s - loss: 0.0286 - accuracy: 0.9911
Epoch 4/20
938/938 - 16s - loss: 0.0243 - accuracy: 0.9924
Epoch 5/20
938/938 - 16s - loss: 0.0196 - accuracy: 0.9939
Epoch 6/20
938/938 - 16s - loss: 0.0177 - accuracy: 0.9945
Epoch 7/20
938/938 - 16s - loss: 0.0159 - accuracy: 0.9947
Epoch 8/20
938/938 - 16s - loss: 0.0133 - accuracy: 0.9961
Epoch 9/20
938/938 - 16s - loss: 0.0122 - accuracy: 0.9964
Epoch 10/20
938/938 - 16s - loss: 0.0116 - accuracy: 0.9964
Epoch 11/20
938/938 - 16s - loss: 0.0074 - accuracy: 0.9978
Epoch 12/20
938/938 - 16s - loss: 0.0128 - accuracy: 0.9961
Epoch 13/20
938/938 - 16s - loss: 0.0074 - accuracy: 0.9978
Epoch 14/20
938/938 - 16s - loss: 0.0095 - accuracy: 0.9971
Epoch 15/20
938/938 - 16s - loss: 0.0120 - accuracy: 0.9966
Epoch 16/20
938/938 - 16s - loss: 0.0066 - accuracy: 0.9981
Epoch 17/20
938/938 - 16s - loss: 0.0092 - accuracy: 0.9976
Epoch 18/20
938/938 - 16s - loss: 0.0049 - accuracy: 0.9987
Epoch 19/20
938/938 - 16s - loss: 0.0118 - accuracy: 0.9970
Epoch 20/20
938/938 - 16s - loss: 0.0041 - accuracy: 0.9988
Model: "model_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
res_block_3 (ResBlock)       (None, 14, 14, 64)        28896     
_________________________________________________________________
res_block_4 (ResBlock)       (None, 7, 7, 256)         592512    
_________________________________________________________________
flatten_5 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 10)                125450    
=================================================================
Total params: 746,858
Trainable params: 745,578
Non-trainable params: 1,280
_________________________________________________________________
None
157/157 - 1s - loss: 0.0389 - accuracy: 0.9931

[0.03885483369231224, 0.9930999875068665]

CUSTOM LAYERS

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

# To Avoid GPU errors
physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28 * 28).astype("float32") / 255.0
x_test = x_test.reshape(-1, 28 * 28).astype("float32") / 255.0


class Dense(layers.Layer):
    print("in dense1")
    def __init__(self, units, input_dim):
        super(Dense, self).__init__()
        self.w = self.add_weight(
            name="w",
            shape=(input_dim, units),
            initializer="random_normal",
            trainable=True,
        )

        self.b = self.add_weight(
            name="b", shape=(units,), initializer="zeros", trainable=True
        )

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b


class Dense(layers.Layer):
    print("in dense2")
    def __init__(self, units):
        super(Dense, self).__init__()
        self.units = units

    def build(self, input_shape):
        self.w = self.add_weight(
            name="w",
            shape=(input_shape[-1], self.units),
            initializer="random_normal",
            trainable=True,
        )
        self.b = self.add_weight(
            name="b", shape=(self.units,), initializer="random_normal", trainable=True,
        )

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b


class MyReLU(layers.Layer):
    def __init__(self):
        super(MyReLU, self).__init__()

    def call(self, x):
        return tf.math.maximum(x, 0)


class MyModel(keras.Model):  # model.fit, model.evalute, model.predict
    def __init__(self, num_classes=10):
        super(MyModel, self).__init__()
        self.dense1 = Dense(64)
        self.dense2 = Dense(num_classes)
        self.relu = MyReLU()

        # self.dense1 = layers.Dense(64)
        # self.dense3 = layers.Dense(num_classes)

    def call(self, x):
        x = self.relu(self.dense1(x))
        return self.dense2(x)


model = MyModel()
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(),
    metrics=["accuracy"],
)

model.fit(x_train, y_train, batch_size=32, epochs=2, verbose=2)
model.evaluate(x_test, y_test, batch_size=32, verbose=2)

in dense1
in dense2
Epoch 1/2
1875/1875 - 4s - loss: 0.3468 - accuracy: 0.9036
Epoch 2/2
1875/1875 - 2s - loss: 0.1615 - accuracy: 0.9527
313/313 - 0s - loss: 0.1433 - accuracy: 0.9549

[0.14334601163864136, 0.9549000263214111]

SAVE & LOADING MODELS

 import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

# To Avoid GPU errors
physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28 * 28).astype("float32") / 255.0
x_test = x_test.reshape(-1, 28 * 28).astype("float32") / 255.0


# 1. How to save and load model weights
# 2. Save and loading entire model (Serializing model)
#   - Saves weights
#   - Model architecture
#   - Training Configuration (model.compile())
#   - Optimizer and states


# method1: Sequential API
model1 = keras.Sequential([layers.Dense(64, activation="relu"), layers.Dense(10)])


# method1: Functional API
inputs = keras.Input(784)
x = layers.Dense(64, activation="relu")(inputs)
outputs = layers.Dense(10)(x)
model2 = keras.Model(inputs=inputs, outputs=outputs)


# Model subclassing
class MyModel(keras.Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = layers.Dense(64, activation="relu")
        self.dense2 = layers.Dense(10)

    def call(self, input_tensor):
        x = tf.nn.relu(self.dense1(input_tensor))
        return self.dense2(x)


# SavedModel format or HDF5 format
model3 = MyModel()
# model = keras.models.load_model('saved_model/')
# model.load_weights('checkpoint_folder/')

model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(),
    metrics=["accuracy"],
)

model.fit(x_train, y_train, batch_size=32, epochs=2, verbose=2)
model.evaluate(x_test, y_test, batch_size=32, verbose=2)
# model.save_weights('checkpoint_folder/')
model.save("saved_model/")