import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

dir(keras.datasets)

['__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'boston_housing',
 'california_housing',
 'cifar10',
 'cifar100',
 'fashion_mnist',
 'imdb',
 'mnist',
 'reuters']

from keras.datasets import mnist

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

((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))

digit_no = 30000
plt.figure(figsize=(1,1))
plt.imshow(x_train[digit_no], cmap="gray")
plt.title(y_train[digit_no])
plt.show()

# normalize: 0,255 -> 0,1
x_train, x_test = x_train / 255.0, x_test / 255.0

from tensorflow.keras.utils import to_categorical

train_labels = to_categorical(y_train)
test_labels = to_categorical(y_test)

train_labels.shape

(60000, 10)

y_train[0]

5

train_labels[0]

array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)

x_train=x_train.reshape((-1,28,28)) x_test=x_test.reshape((-1,28,28))

# model
model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=(28,28,1)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation= 'softmax'),
])
model.summary()

Model: "sequential_5"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten_5 (Flatten)         (None, 784)               0         
                                                                 
 dense_10 (Dense)            (None, 128)               100480    
                                                                 
 dense_11 (Dense)            (None, 10)                1290      
                                                                 
=================================================================
Total params: 101,770
Trainable params: 101,770
Non-trainable params: 0
_________________________________________________________________

model.compile(optimizer = 'adam',
             loss = 'categorical_crossentropy',
             metrics = ['accuracy'])

history=model.fit(x_train, train_labels, validation_data=(x_test, test_labels), epochs=5, batch_size = 1024)

Epoch 1/5
59/59 [==============================] - 1s 9ms/step - loss: 0.8051 - accuracy: 0.7909 - val_loss: 0.3538 - val_accuracy: 0.9026
Epoch 2/5
59/59 [==============================] - 0s 5ms/step - loss: 0.3166 - accuracy: 0.9119 - val_loss: 0.2725 - val_accuracy: 0.9233
Epoch 3/5
59/59 [==============================] - 0s 5ms/step - loss: 0.2551 - accuracy: 0.9287 - val_loss: 0.2278 - val_accuracy: 0.9357
Epoch 4/5
59/59 [==============================] - 0s 5ms/step - loss: 0.2159 - accuracy: 0.9398 - val_loss: 0.2019 - val_accuracy: 0.9409
Epoch 5/5
59/59 [==============================] - 0s 6ms/step - loss: 0.1867 - accuracy: 0.9480 - val_loss: 0.1761 - val_accuracy: 0.9489

model.evaluate(x_test, test_labels)

313/313 [==============================] - 1s 4ms/step - loss: 0.1761 - accuracy: 0.9489





[0.17605803906917572, 0.9488999843597412]

digit_no = 10000
plt.figure(figsize=(1,1))
plt.imshow(x_train[digit_no], cmap="gray")
plt.title(y_train[digit_no])
plt.show()

x_train[digit_no].shape

(28, 28)

predictions=model.predict(x_train[digit_no].reshape(-1,28,28))
predictions

1/1 [==============================] - 0s 25ms/step





array([[8.6974643e-07, 2.3044556e-07, 1.4908462e-06, 9.9979752e-01,
        5.0020077e-07, 1.0159187e-04, 5.4114313e-09, 6.0411563e-05,
        9.3489880e-06, 2.7979790e-05]], dtype=float32)

predictions.sum()

0.9999999

label_prediction = np.argmax(predictions)
label_prediction

3

predictions=model.predict(x_train[digit_no:digit_no+5].reshape(-1,28,28))
predictions

1/1 [==============================] - 0s 25ms/step





array([[8.69449764e-07, 2.30259332e-07, 1.48977779e-06, 9.99797761e-01,
        5.00648980e-07, 1.01517027e-04, 5.40599565e-09, 6.04217166e-05,
        9.36473134e-06, 2.79820888e-05],
       [5.89868905e-05, 3.73386499e-03, 5.78068604e-04, 2.68029445e-03,
        3.54710983e-06, 7.93180297e-05, 7.16538416e-05, 9.05721481e-06,
        9.92584348e-01, 2.00937851e-04],
       [9.87021243e-10, 6.97708025e-09, 1.05508334e-04, 5.96734462e-04,
        5.28718935e-09, 9.09166076e-08, 1.16057216e-11, 9.99281704e-01,
        4.03025479e-06, 1.19101687e-05],
       [2.78679872e-05, 9.01423264e-05, 2.23633310e-06, 2.02506359e-04,
        2.04386637e-01, 5.77627961e-03, 3.20659819e-05, 9.82213859e-03,
        8.61406661e-05, 7.79573977e-01],
       [3.94474664e-05, 5.47840773e-06, 1.39120535e-03, 5.03121968e-03,
        7.80644501e-03, 1.19754886e-02, 6.83387552e-05, 4.79648598e-02,
        1.25806406e-03, 9.24459457e-01]], dtype=float32)

label_prediction = np.argmax(predictions,axis=1)
label_prediction

array([3, 8, 7, 9, 9], dtype=int64)

y_train[10000:10005]

array([3, 8, 7, 9, 9], dtype=uint8)