tidystep - Tensorflow in python 2:Fashion MNIST

1 about the data

This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:

The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The labels are an array of integers, ranging from 0 to 9. These correspond to the class of clothing the image represents:

2 package

Code

import numpy as np
import matplotlib.pyplot as plt
import os
import tensorflow as tf
print("TensorFlow version:", tf.__version__)

TensorFlow version: 2.14.0

3 input data

Code

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

Code

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

3.1 x_train:60000 pic with 28 by 28 pixel

Code

train_images.shape

(60000, 28, 28)

3.2 x_test:10000 pic with 28 by 28 pixel

Code

test_images.shape

(10000, 28, 28)

3.3 y_train:60000 pic lable

Code

train_labels.shape

(60000,)

almost each number have ~6000 so total 60,000 on training

Code

unique, counts = np.unique(train_labels, return_counts=True)
dict(zip(unique, counts))

{0: 6000,
 1: 6000,
 2: 6000,
 3: 6000,
 4: 6000,
 5: 6000,
 6: 6000,
 7: 6000,
 8: 6000,
 9: 6000}

3.4 y_train:10000 pic lable

Code

test_labels.shape

(10000,)

first train pic

Code

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

4 data clean

Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It’s important that the training set and the testing set be preprocessed in the same way:

Code

train_images = train_images / 255.0

test_images = test_images / 255.0

5 bulid model

5.1 model

Code

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

Optimizer —This is how the model is updated based on the data it sees and its loss function.

Loss function —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.

Metrics —Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified.

5.2 loss function

Code

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

5.3 compile

Code

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

Code

# Show the model architecture
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten (Flatten)           (None, 784)               0         
                                                                 
 dense (Dense)               (None, 128)               100480    
                                                                 
 dense_1 (Dense)             (None, 10)                1290      
                                                                 
=================================================================
Total params: 101770 (397.54 KB)
Trainable params: 101770 (397.54 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

5.4 traning

Code

model.fit(train_images, train_labels, epochs=5)

Epoch 1/5
   1/1875 [..............................] - ETA: 3:19 - loss: 2.4175 - accuracy: 0.1562  99/1875 [>.............................] - ETA: 0s - loss: 0.9238 - accuracy: 0.6872   203/1875 [==>...........................] - ETA: 0s - loss: 0.7725 - accuracy: 0.7377 308/1875 [===>..........................] - ETA: 0s - loss: 0.6927 - accuracy: 0.7623 413/1875 [=====>........................] - ETA: 0s - loss: 0.6524 - accuracy: 0.7749 517/1875 [=======>......................] - ETA: 0s - loss: 0.6313 - accuracy: 0.7815 622/1875 [========>.....................] - ETA: 0s - loss: 0.6106 - accuracy: 0.7871 727/1875 [==========>...................] - ETA: 0s - loss: 0.5915 - accuracy: 0.7932 832/1875 [============>.................] - ETA: 0s - loss: 0.5755 - accuracy: 0.7980 937/1875 [=============>................] - ETA: 0s - loss: 0.5658 - accuracy: 0.80131042/1875 [===============>..............] - ETA: 0s - loss: 0.5540 - accuracy: 0.80561147/1875 [=================>............] - ETA: 0s - loss: 0.5444 - accuracy: 0.80891253/1875 [===================>..........] - ETA: 0s - loss: 0.5355 - accuracy: 0.81171358/1875 [====================>.........] - ETA: 0s - loss: 0.5256 - accuracy: 0.81481464/1875 [======================>.......] - ETA: 0s - loss: 0.5191 - accuracy: 0.81641570/1875 [========================>.....] - ETA: 0s - loss: 0.5136 - accuracy: 0.81821676/1875 [=========================>....] - ETA: 0s - loss: 0.5076 - accuracy: 0.82041782/1875 [===========================>..] - ETA: 0s - loss: 0.5006 - accuracy: 0.82271875/1875 [==============================] - 1s 480us/step - loss: 0.4980 - accuracy: 0.8235
Epoch 2/5
   1/1875 [..............................] - ETA: 1s - loss: 0.3227 - accuracy: 0.9062  99/1875 [>.............................] - ETA: 0s - loss: 0.3789 - accuracy: 0.8602 203/1875 [==>...........................] - ETA: 0s - loss: 0.3903 - accuracy: 0.8615 309/1875 [===>..........................] - ETA: 0s - loss: 0.3988 - accuracy: 0.8582 414/1875 [=====>........................] - ETA: 0s - loss: 0.3952 - accuracy: 0.8598 520/1875 [=======>......................] - ETA: 0s - loss: 0.3909 - accuracy: 0.8611 626/1875 [=========>....................] - ETA: 0s - loss: 0.3847 - accuracy: 0.8627 731/1875 [==========>...................] - ETA: 0s - loss: 0.3870 - accuracy: 0.8620 836/1875 [============>.................] - ETA: 0s - loss: 0.3803 - accuracy: 0.8644 941/1875 [==============>...............] - ETA: 0s - loss: 0.3813 - accuracy: 0.86381047/1875 [===============>..............] - ETA: 0s - loss: 0.3790 - accuracy: 0.86491152/1875 [=================>............] - ETA: 0s - loss: 0.3797 - accuracy: 0.86431258/1875 [===================>..........] - ETA: 0s - loss: 0.3794 - accuracy: 0.86381364/1875 [====================>.........] - ETA: 0s - loss: 0.3789 - accuracy: 0.86381471/1875 [======================>.......] - ETA: 0s - loss: 0.3774 - accuracy: 0.86451577/1875 [========================>.....] - ETA: 0s - loss: 0.3769 - accuracy: 0.86441683/1875 [=========================>....] - ETA: 0s - loss: 0.3757 - accuracy: 0.86421789/1875 [===========================>..] - ETA: 0s - loss: 0.3739 - accuracy: 0.86511875/1875 [==============================] - 1s 477us/step - loss: 0.3737 - accuracy: 0.8650
Epoch 3/5
   1/1875 [..............................] - ETA: 1s - loss: 0.2166 - accuracy: 0.9375 110/1875 [>.............................] - ETA: 0s - loss: 0.3621 - accuracy: 0.8625 218/1875 [==>...........................] - ETA: 0s - loss: 0.3563 - accuracy: 0.8651 328/1875 [====>.........................] - ETA: 0s - loss: 0.3533 - accuracy: 0.8699 435/1875 [=====>........................] - ETA: 0s - loss: 0.3475 - accuracy: 0.8731 542/1875 [=======>......................] - ETA: 0s - loss: 0.3455 - accuracy: 0.8723 648/1875 [=========>....................] - ETA: 0s - loss: 0.3432 - accuracy: 0.8723 756/1875 [===========>..................] - ETA: 0s - loss: 0.3409 - accuracy: 0.8739 864/1875 [============>.................] - ETA: 0s - loss: 0.3414 - accuracy: 0.8737 971/1875 [==============>...............] - ETA: 0s - loss: 0.3412 - accuracy: 0.87401080/1875 [================>.............] - ETA: 0s - loss: 0.3383 - accuracy: 0.87511188/1875 [==================>...........] - ETA: 0s - loss: 0.3364 - accuracy: 0.87561295/1875 [===================>..........] - ETA: 0s - loss: 0.3375 - accuracy: 0.87541402/1875 [=====================>........] - ETA: 0s - loss: 0.3384 - accuracy: 0.87531508/1875 [=======================>......] - ETA: 0s - loss: 0.3372 - accuracy: 0.87561619/1875 [========================>.....] - ETA: 0s - loss: 0.3363 - accuracy: 0.87581725/1875 [==========================>...] - ETA: 0s - loss: 0.3371 - accuracy: 0.87531830/1875 [============================>.] - ETA: 0s - loss: 0.3365 - accuracy: 0.87571875/1875 [==============================] - 1s 468us/step - loss: 0.3371 - accuracy: 0.8757
Epoch 4/5
   1/1875 [..............................] - ETA: 1s - loss: 0.2570 - accuracy: 0.9375 107/1875 [>.............................] - ETA: 0s - loss: 0.3284 - accuracy: 0.8835 212/1875 [==>...........................] - ETA: 0s - loss: 0.3300 - accuracy: 0.8784 318/1875 [====>.........................] - ETA: 0s - loss: 0.3293 - accuracy: 0.8781 424/1875 [=====>........................] - ETA: 0s - loss: 0.3242 - accuracy: 0.8808 526/1875 [=======>......................] - ETA: 0s - loss: 0.3223 - accuracy: 0.8816 629/1875 [=========>....................] - ETA: 0s - loss: 0.3190 - accuracy: 0.8828 734/1875 [==========>...................] - ETA: 0s - loss: 0.3171 - accuracy: 0.8841 840/1875 [============>.................] - ETA: 0s - loss: 0.3149 - accuracy: 0.8846 946/1875 [==============>...............] - ETA: 0s - loss: 0.3134 - accuracy: 0.88621052/1875 [===============>..............] - ETA: 0s - loss: 0.3131 - accuracy: 0.88641156/1875 [=================>............] - ETA: 0s - loss: 0.3134 - accuracy: 0.88591261/1875 [===================>..........] - ETA: 0s - loss: 0.3118 - accuracy: 0.88621366/1875 [====================>.........] - ETA: 0s - loss: 0.3115 - accuracy: 0.88631471/1875 [======================>.......] - ETA: 0s - loss: 0.3126 - accuracy: 0.88501576/1875 [========================>.....] - ETA: 0s - loss: 0.3122 - accuracy: 0.88521682/1875 [=========================>....] - ETA: 0s - loss: 0.3118 - accuracy: 0.88521786/1875 [===========================>..] - ETA: 0s - loss: 0.3114 - accuracy: 0.88511875/1875 [==============================] - 1s 479us/step - loss: 0.3122 - accuracy: 0.8848
Epoch 5/5
   1/1875 [..............................] - ETA: 1s - loss: 0.3907 - accuracy: 0.8438 105/1875 [>.............................] - ETA: 0s - loss: 0.3071 - accuracy: 0.8830 208/1875 [==>...........................] - ETA: 0s - loss: 0.3041 - accuracy: 0.8851 312/1875 [===>..........................] - ETA: 0s - loss: 0.3007 - accuracy: 0.8864 416/1875 [=====>........................] - ETA: 0s - loss: 0.3013 - accuracy: 0.8877 521/1875 [=======>......................] - ETA: 0s - loss: 0.3009 - accuracy: 0.8874 626/1875 [=========>....................] - ETA: 0s - loss: 0.2973 - accuracy: 0.8887 731/1875 [==========>...................] - ETA: 0s - loss: 0.2961 - accuracy: 0.8895 835/1875 [============>.................] - ETA: 0s - loss: 0.2966 - accuracy: 0.8894 940/1875 [==============>...............] - ETA: 0s - loss: 0.2948 - accuracy: 0.89031045/1875 [===============>..............] - ETA: 0s - loss: 0.2941 - accuracy: 0.89061151/1875 [=================>............] - ETA: 0s - loss: 0.2934 - accuracy: 0.89091257/1875 [===================>..........] - ETA: 0s - loss: 0.2950 - accuracy: 0.89101363/1875 [====================>.........] - ETA: 0s - loss: 0.2960 - accuracy: 0.89101468/1875 [======================>.......] - ETA: 0s - loss: 0.2959 - accuracy: 0.89081574/1875 [========================>.....] - ETA: 0s - loss: 0.2962 - accuracy: 0.89071676/1875 [=========================>....] - ETA: 0s - loss: 0.2971 - accuracy: 0.89031781/1875 [===========================>..] - ETA: 0s - loss: 0.2959 - accuracy: 0.89091875/1875 [==============================] - 1s 480us/step - loss: 0.2952 - accuracy: 0.8910

<keras.src.callbacks.History at 0x1533d7a10>

5.5 add call back

Code

class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if (logs.get('accuracy') > 0.95):
                print("\n Reached its having 95% Accuracy!!!!!!!")
                self.model.stop_training = True   
                
callbacks = myCallback()

its stop on 34 epochs since it reach 95% Accuracy

Code

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

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

history = model.fit(train_images, train_labels
                      ,validation_data=(test_images, test_labels)
                    ,epochs=50,callbacks=[callbacks])

Epoch 1/50
   1/1875 [..............................] - ETA: 2:48 - loss: 2.4040 - accuracy: 0.0938  99/1875 [>.............................] - ETA: 0s - loss: 0.9319 - accuracy: 0.6834   212/1875 [==>...........................] - ETA: 0s - loss: 0.7658 - accuracy: 0.7370 319/1875 [====>.........................] - ETA: 0s - loss: 0.6910 - accuracy: 0.7601 426/1875 [=====>........................] - ETA: 0s - loss: 0.6483 - accuracy: 0.7749 531/1875 [=======>......................] - ETA: 0s - loss: 0.6168 - accuracy: 0.7846 638/1875 [=========>....................] - ETA: 0s - loss: 0.5997 - accuracy: 0.7914 745/1875 [==========>...................] - ETA: 0s - loss: 0.5829 - accuracy: 0.7963 851/1875 [============>.................] - ETA: 0s - loss: 0.5691 - accuracy: 0.8009 958/1875 [==============>...............] - ETA: 0s - loss: 0.5577 - accuracy: 0.80511071/1875 [================>.............] - ETA: 0s - loss: 0.5479 - accuracy: 0.80861182/1875 [=================>............] - ETA: 0s - loss: 0.5361 - accuracy: 0.81261289/1875 [===================>..........] - ETA: 0s - loss: 0.5282 - accuracy: 0.81501399/1875 [=====================>........] - ETA: 0s - loss: 0.5206 - accuracy: 0.81791505/1875 [=======================>......] - ETA: 0s - loss: 0.5142 - accuracy: 0.81991613/1875 [========================>.....] - ETA: 0s - loss: 0.5082 - accuracy: 0.82211719/1875 [==========================>...] - ETA: 0s - loss: 0.5026 - accuracy: 0.82421824/1875 [============================>.] - ETA: 0s - loss: 0.4977 - accuracy: 0.82571875/1875 [==============================] - 1s 608us/step - loss: 0.4961 - accuracy: 0.8262 - val_loss: 0.4306 - val_accuracy: 0.8504
Epoch 2/50
   1/1875 [..............................] - ETA: 1s - loss: 0.1758 - accuracy: 0.9375 106/1875 [>.............................] - ETA: 0s - loss: 0.3694 - accuracy: 0.8653 212/1875 [==>...........................] - ETA: 0s - loss: 0.3691 - accuracy: 0.8687 319/1875 [====>.........................] - ETA: 0s - loss: 0.3807 - accuracy: 0.8637 424/1875 [=====>........................] - ETA: 0s - loss: 0.3774 - accuracy: 0.8641 531/1875 [=======>......................] - ETA: 0s - loss: 0.3798 - accuracy: 0.8628 642/1875 [=========>....................] - ETA: 0s - loss: 0.3773 - accuracy: 0.8631 748/1875 [==========>...................] - ETA: 0s - loss: 0.3734 - accuracy: 0.8655 856/1875 [============>.................] - ETA: 0s - loss: 0.3741 - accuracy: 0.8650 965/1875 [==============>...............] - ETA: 0s - loss: 0.3745 - accuracy: 0.86401072/1875 [================>.............] - ETA: 0s - loss: 0.3748 - accuracy: 0.86441179/1875 [=================>............] - ETA: 0s - loss: 0.3753 - accuracy: 0.86391287/1875 [===================>..........] - ETA: 0s - loss: 0.3744 - accuracy: 0.86401394/1875 [=====================>........] - ETA: 0s - loss: 0.3763 - accuracy: 0.86411501/1875 [=======================>......] - ETA: 0s - loss: 0.3769 - accuracy: 0.86431609/1875 [========================>.....] - ETA: 0s - loss: 0.3745 - accuracy: 0.86511717/1875 [==========================>...] - ETA: 0s - loss: 0.3734 - accuracy: 0.86531825/1875 [============================>.] - ETA: 0s - loss: 0.3737 - accuracy: 0.86521875/1875 [==============================] - 1s 523us/step - loss: 0.3734 - accuracy: 0.8651 - val_loss: 0.4157 - val_accuracy: 0.8477
Epoch 3/50
   1/1875 [..............................] - ETA: 1s - loss: 0.2406 - accuracy: 0.9062 108/1875 [>.............................] - ETA: 0s - loss: 0.3424 - accuracy: 0.8712 216/1875 [==>...........................] - ETA: 0s - loss: 0.3498 - accuracy: 0.8701 324/1875 [====>.........................] - ETA: 0s - loss: 0.3576 - accuracy: 0.8684 433/1875 [=====>........................] - ETA: 0s - loss: 0.3558 - accuracy: 0.8693 541/1875 [=======>......................] - ETA: 0s - loss: 0.3519 - accuracy: 0.8711 649/1875 [=========>....................] - ETA: 0s - loss: 0.3492 - accuracy: 0.8716 756/1875 [===========>..................] - ETA: 0s - loss: 0.3456 - accuracy: 0.8740 864/1875 [============>.................] - ETA: 0s - loss: 0.3424 - accuracy: 0.8753 970/1875 [==============>...............] - ETA: 0s - loss: 0.3409 - accuracy: 0.87541076/1875 [================>.............] - ETA: 0s - loss: 0.3426 - accuracy: 0.87521183/1875 [=================>............] - ETA: 0s - loss: 0.3417 - accuracy: 0.87531291/1875 [===================>..........] - ETA: 0s - loss: 0.3393 - accuracy: 0.87621398/1875 [=====================>........] - ETA: 0s - loss: 0.3398 - accuracy: 0.87631506/1875 [=======================>......] - ETA: 0s - loss: 0.3364 - accuracy: 0.87771613/1875 [========================>.....] - ETA: 0s - loss: 0.3366 - accuracy: 0.87771719/1875 [==========================>...] - ETA: 0s - loss: 0.3359 - accuracy: 0.87801826/1875 [============================>.] - ETA: 0s - loss: 0.3360 - accuracy: 0.87781875/1875 [==============================] - 1s 523us/step - loss: 0.3355 - accuracy: 0.8778 - val_loss: 0.3901 - val_accuracy: 0.8646
Epoch 4/50
   1/1875 [..............................] - ETA: 1s - loss: 0.3782 - accuracy: 0.8750 107/1875 [>.............................] - ETA: 0s - loss: 0.3336 - accuracy: 0.8785 214/1875 [==>...........................] - ETA: 0s - loss: 0.3233 - accuracy: 0.8822 322/1875 [====>.........................] - ETA: 0s - loss: 0.3191 - accuracy: 0.8843 427/1875 [=====>........................] - ETA: 0s - loss: 0.3163 - accuracy: 0.8866 533/1875 [=======>......................] - ETA: 0s - loss: 0.3148 - accuracy: 0.8878 640/1875 [=========>....................] - ETA: 0s - loss: 0.3170 - accuracy: 0.8861 745/1875 [==========>...................] - ETA: 0s - loss: 0.3140 - accuracy: 0.8872 848/1875 [============>.................] - ETA: 0s - loss: 0.3127 - accuracy: 0.8879 953/1875 [==============>...............] - ETA: 0s - loss: 0.3129 - accuracy: 0.88821060/1875 [===============>..............] - ETA: 0s - loss: 0.3107 - accuracy: 0.88881167/1875 [=================>............] - ETA: 0s - loss: 0.3111 - accuracy: 0.88851274/1875 [===================>..........] - ETA: 0s - loss: 0.3119 - accuracy: 0.88781379/1875 [=====================>........] - ETA: 0s - loss: 0.3133 - accuracy: 0.88631487/1875 [======================>.......] - ETA: 0s - loss: 0.3140 - accuracy: 0.88591592/1875 [========================>.....] - ETA: 0s - loss: 0.3136 - accuracy: 0.88601696/1875 [==========================>...] - ETA: 0s - loss: 0.3137 - accuracy: 0.88601801/1875 [===========================>..] - ETA: 0s - loss: 0.3122 - accuracy: 0.88661875/1875 [==============================] - 1s 531us/step - loss: 0.3119 - accuracy: 0.8866 - val_loss: 0.3583 - val_accuracy: 0.8739
Epoch 5/50
   1/1875 [..............................] - ETA: 1s - loss: 0.2967 - accuracy: 0.8438 108/1875 [>.............................] - ETA: 0s - loss: 0.2851 - accuracy: 0.9005 215/1875 [==>...........................] - ETA: 0s - loss: 0.2919 - accuracy: 0.8940 321/1875 [====>.........................] - ETA: 0s - loss: 0.2985 - accuracy: 0.8888 427/1875 [=====>........................] - ETA: 0s - loss: 0.2985 - accuracy: 0.8901 539/1875 [=======>......................] - ETA: 0s - loss: 0.3010 - accuracy: 0.8901 645/1875 [=========>....................] - ETA: 0s - loss: 0.2990 - accuracy: 0.8908 750/1875 [===========>..................] - ETA: 0s - loss: 0.3008 - accuracy: 0.8904 850/1875 [============>.................] - ETA: 0s - loss: 0.3001 - accuracy: 0.8910 951/1875 [==============>...............] - ETA: 0s - loss: 0.2988 - accuracy: 0.89121061/1875 [===============>..............] - ETA: 0s - loss: 0.2977 - accuracy: 0.89111170/1875 [=================>............] - ETA: 0s - loss: 0.2962 - accuracy: 0.89211275/1875 [===================>..........] - ETA: 0s - loss: 0.2960 - accuracy: 0.89201381/1875 [=====================>........] - ETA: 0s - loss: 0.2946 - accuracy: 0.89231487/1875 [======================>.......] - ETA: 0s - loss: 0.2950 - accuracy: 0.89221596/1875 [========================>.....] - ETA: 0s - loss: 0.2934 - accuracy: 0.89271704/1875 [==========================>...] - ETA: 0s - loss: 0.2930 - accuracy: 0.89261812/1875 [===========================>..] - ETA: 0s - loss: 0.2924 - accuracy: 0.89311875/1875 [==============================] - 1s 525us/step - loss: 0.2928 - accuracy: 0.8928 - val_loss: 0.3584 - val_accuracy: 0.8719
Epoch 6/50
   1/1875 [..............................] - ETA: 1s - loss: 0.1930 - accuracy: 0.9375 111/1875 [>.............................] - ETA: 0s - loss: 0.2888 - accuracy: 0.9023 217/1875 [==>...........................] - ETA: 0s - loss: 0.2812 - accuracy: 0.9001 323/1875 [====>.........................] - ETA: 0s - loss: 0.2804 - accuracy: 0.8999 426/1875 [=====>........................] - ETA: 0s - loss: 0.2805 - accuracy: 0.8973 528/1875 [=======>......................] - ETA: 0s - loss: 0.2806 - accuracy: 0.8962 623/1875 [========>.....................] - ETA: 0s - loss: 0.2801 - accuracy: 0.8962 725/1875 [==========>...................] - ETA: 0s - loss: 0.2789 - accuracy: 0.8971 828/1875 [============>.................] - ETA: 0s - loss: 0.2793 - accuracy: 0.8977 930/1875 [=============>................] - ETA: 0s - loss: 0.2787 - accuracy: 0.89801032/1875 [===============>..............] - ETA: 0s - loss: 0.2777 - accuracy: 0.89871130/1875 [=================>............] - ETA: 0s - loss: 0.2804 - accuracy: 0.89771234/1875 [==================>...........] - ETA: 0s - loss: 0.2791 - accuracy: 0.89861339/1875 [====================>.........] - ETA: 0s - loss: 0.2786 - accuracy: 0.89891445/1875 [======================>.......] - ETA: 0s - loss: 0.2799 - accuracy: 0.89821550/1875 [=======================>......] - ETA: 0s - loss: 0.2793 - accuracy: 0.89811656/1875 [=========================>....] - ETA: 0s - loss: 0.2790 - accuracy: 0.89821762/1875 [===========================>..] - ETA: 0s - loss: 0.2801 - accuracy: 0.89801810/1875 [===========================>..] - ETA: 0s - loss: 0.2798 - accuracy: 0.89801875/1875 [==============================] - 1s 559us/step - loss: 0.2796 - accuracy: 0.8979 - val_loss: 0.3511 - val_accuracy: 0.8783
Epoch 7/50
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Epoch 8/50
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Epoch 9/50
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Epoch 10/50
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Epoch 11/50
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Epoch 12/50
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Epoch 13/50
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Epoch 14/50
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Epoch 15/50
   1/1875 [..............................] - ETA: 1s - loss: 0.1196 - accuracy: 0.9688 106/1875 [>.............................] - ETA: 0s - loss: 0.1984 - accuracy: 0.9251 212/1875 [==>...........................] - ETA: 0s - loss: 0.2003 - accuracy: 0.9261 319/1875 [====>.........................] - ETA: 0s - loss: 0.2014 - accuracy: 0.9246 424/1875 [=====>........................] - ETA: 0s - loss: 0.2003 - accuracy: 0.9238 528/1875 [=======>......................] - ETA: 0s - loss: 0.2014 - accuracy: 0.9231 635/1875 [=========>....................] - ETA: 0s - loss: 0.1993 - accuracy: 0.9244 741/1875 [==========>...................] - ETA: 0s - loss: 0.2009 - accuracy: 0.9237 845/1875 [============>.................] - ETA: 0s - loss: 0.2003 - accuracy: 0.9240 950/1875 [==============>...............] - ETA: 0s - loss: 0.1996 - accuracy: 0.92431055/1875 [===============>..............] - ETA: 0s - loss: 0.2021 - accuracy: 0.92321161/1875 [=================>............] - ETA: 0s - loss: 0.2024 - accuracy: 0.92321267/1875 [===================>..........] - ETA: 0s - loss: 0.2024 - accuracy: 0.92351373/1875 [====================>.........] - ETA: 0s - loss: 0.2020 - accuracy: 0.92401479/1875 [======================>.......] - ETA: 0s - loss: 0.2019 - accuracy: 0.92411583/1875 [========================>.....] - ETA: 0s - loss: 0.2001 - accuracy: 0.92491690/1875 [==========================>...] - ETA: 0s - loss: 0.2004 - accuracy: 0.92491796/1875 [===========================>..] - ETA: 0s - loss: 0.2013 - accuracy: 0.92431875/1875 [==============================] - 1s 532us/step - loss: 0.2014 - accuracy: 0.9243 - val_loss: 0.3532 - val_accuracy: 0.8836
Epoch 16/50
   1/1875 [..............................] - ETA: 1s - loss: 0.2534 - accuracy: 0.8438 105/1875 [>.............................] - ETA: 0s - loss: 0.2024 - accuracy: 0.9229 210/1875 [==>...........................] - ETA: 0s - loss: 0.1941 - accuracy: 0.9256 315/1875 [====>.........................] - ETA: 0s - loss: 0.1910 - accuracy: 0.9273 421/1875 [=====>........................] - ETA: 0s - loss: 0.1868 - accuracy: 0.9299 525/1875 [=======>......................] - ETA: 0s - loss: 0.1902 - accuracy: 0.9283 629/1875 [=========>....................] - ETA: 0s - loss: 0.1917 - accuracy: 0.9275 733/1875 [==========>...................] - ETA: 0s - loss: 0.1916 - accuracy: 0.9282 838/1875 [============>.................] - ETA: 0s - loss: 0.1920 - accuracy: 0.9273 945/1875 [==============>...............] - ETA: 0s - loss: 0.1910 - accuracy: 0.92791053/1875 [===============>..............] - ETA: 0s - loss: 0.1908 - accuracy: 0.92851160/1875 [=================>............] - ETA: 0s - loss: 0.1914 - accuracy: 0.92841266/1875 [===================>..........] - ETA: 0s - loss: 0.1930 - accuracy: 0.92781371/1875 [====================>.........] - ETA: 0s - loss: 0.1942 - accuracy: 0.92711480/1875 [======================>.......] - ETA: 0s - loss: 0.1949 - accuracy: 0.92691589/1875 [========================>.....] - ETA: 0s - loss: 0.1952 - accuracy: 0.92691698/1875 [==========================>...] - ETA: 0s - loss: 0.1958 - accuracy: 0.92681804/1875 [===========================>..] - ETA: 0s - loss: 0.1963 - accuracy: 0.92641875/1875 [==============================] - 1s 535us/step - loss: 0.1964 - accuracy: 0.9264 - val_loss: 0.3350 - val_accuracy: 0.8907
Epoch 17/50
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Epoch 18/50
   1/1875 [..............................] - ETA: 1s - loss: 0.1059 - accuracy: 0.9375 110/1875 [>.............................] - ETA: 0s - loss: 0.1798 - accuracy: 0.9307 220/1875 [==>...........................] - ETA: 0s - loss: 0.1737 - accuracy: 0.9328 331/1875 [====>.........................] - ETA: 0s - loss: 0.1744 - accuracy: 0.9334 442/1875 [======>.......................] - ETA: 0s - loss: 0.1763 - accuracy: 0.9342 554/1875 [=======>......................] - ETA: 0s - loss: 0.1773 - accuracy: 0.9340 661/1875 [=========>....................] - ETA: 0s - loss: 0.1771 - accuracy: 0.9336 771/1875 [===========>..................] - ETA: 0s - loss: 0.1814 - accuracy: 0.9318 883/1875 [=============>................] - ETA: 0s - loss: 0.1829 - accuracy: 0.9312 995/1875 [==============>...............] - ETA: 0s - loss: 0.1833 - accuracy: 0.93131107/1875 [================>.............] - ETA: 0s - loss: 0.1837 - accuracy: 0.93121218/1875 [==================>...........] - ETA: 0s - loss: 0.1837 - accuracy: 0.93151330/1875 [====================>.........] - ETA: 0s - loss: 0.1846 - accuracy: 0.93111440/1875 [======================>.......] - ETA: 0s - loss: 0.1846 - accuracy: 0.93091552/1875 [=======================>......] - ETA: 0s - loss: 0.1843 - accuracy: 0.93111664/1875 [=========================>....] - ETA: 0s - loss: 0.1844 - accuracy: 0.93101776/1875 [===========================>..] - ETA: 0s - loss: 0.1857 - accuracy: 0.93041875/1875 [==============================] - 1s 506us/step - loss: 0.1860 - accuracy: 0.9301 - val_loss: 0.3470 - val_accuracy: 0.8857
Epoch 19/50
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Epoch 20/50
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Epoch 21/50
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Epoch 22/50
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Epoch 23/50
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Epoch 24/50
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Epoch 25/50
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Epoch 26/50
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Epoch 27/50
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Epoch 28/50
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Epoch 29/50
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Epoch 30/50
   1/1875 [..............................] - ETA: 1s - loss: 0.0880 - accuracy: 0.9688 106/1875 [>.............................] - ETA: 0s - loss: 0.1366 - accuracy: 0.9443 196/1875 [==>...........................] - ETA: 0s - loss: 0.1335 - accuracy: 0.9455 298/1875 [===>..........................] - ETA: 0s - loss: 0.1364 - accuracy: 0.9466 401/1875 [=====>........................] - ETA: 0s - loss: 0.1351 - accuracy: 0.9482 502/1875 [=======>......................] - ETA: 0s - loss: 0.1356 - accuracy: 0.9480 601/1875 [========>.....................] - ETA: 0s - loss: 0.1358 - accuracy: 0.9480 700/1875 [==========>...................] - ETA: 0s - loss: 0.1353 - accuracy: 0.9484 804/1875 [===========>..................] - ETA: 0s - loss: 0.1355 - accuracy: 0.9483 908/1875 [=============>................] - ETA: 0s - loss: 0.1368 - accuracy: 0.94821013/1875 [===============>..............] - ETA: 0s - loss: 0.1369 - accuracy: 0.94871119/1875 [================>.............] - ETA: 0s - loss: 0.1359 - accuracy: 0.94891226/1875 [==================>...........] - ETA: 0s - loss: 0.1368 - accuracy: 0.94861330/1875 [====================>.........] - ETA: 0s - loss: 0.1375 - accuracy: 0.94841435/1875 [=====================>........] - ETA: 0s - loss: 0.1384 - accuracy: 0.94801540/1875 [=======================>......] - ETA: 0s - loss: 0.1387 - accuracy: 0.94821646/1875 [=========================>....] - ETA: 0s - loss: 0.1403 - accuracy: 0.94771753/1875 [===========================>..] - ETA: 0s - loss: 0.1412 - accuracy: 0.94741859/1875 [============================>.] - ETA: 0s - loss: 0.1412 - accuracy: 0.94761875/1875 [==============================] - 1s 542us/step - loss: 0.1409 - accuracy: 0.9476 - val_loss: 0.4171 - val_accuracy: 0.8896
Epoch 31/50
   1/1875 [..............................] - ETA: 1s - loss: 0.0201 - accuracy: 1.0000 106/1875 [>.............................] - ETA: 0s - loss: 0.1396 - accuracy: 0.9502 213/1875 [==>...........................] - ETA: 0s - loss: 0.1299 - accuracy: 0.9529 320/1875 [====>.........................] - ETA: 0s - loss: 0.1298 - accuracy: 0.9517 422/1875 [=====>........................] - ETA: 0s - loss: 0.1288 - accuracy: 0.9516 529/1875 [=======>......................] - ETA: 0s - loss: 0.1322 - accuracy: 0.9496 636/1875 [=========>....................] - ETA: 0s - loss: 0.1336 - accuracy: 0.9491 743/1875 [==========>...................] - ETA: 0s - loss: 0.1345 - accuracy: 0.9493 849/1875 [============>.................] - ETA: 0s - loss: 0.1340 - accuracy: 0.9496 954/1875 [==============>...............] - ETA: 0s - loss: 0.1348 - accuracy: 0.94931061/1875 [===============>..............] - ETA: 0s - loss: 0.1365 - accuracy: 0.94861167/1875 [=================>............] - ETA: 0s - loss: 0.1367 - accuracy: 0.94831275/1875 [===================>..........] - ETA: 0s - loss: 0.1363 - accuracy: 0.94881381/1875 [=====================>........] - ETA: 0s - loss: 0.1371 - accuracy: 0.94881489/1875 [======================>.......] - ETA: 0s - loss: 0.1370 - accuracy: 0.94911596/1875 [========================>.....] - ETA: 0s - loss: 0.1376 - accuracy: 0.94871704/1875 [==========================>...] - ETA: 0s - loss: 0.1383 - accuracy: 0.94841810/1875 [===========================>..] - ETA: 0s - loss: 0.1383 - accuracy: 0.94831875/1875 [==============================] - 1s 528us/step - loss: 0.1384 - accuracy: 0.9484 - val_loss: 0.4222 - val_accuracy: 0.8811
Epoch 32/50
   1/1875 [..............................] - ETA: 1s - loss: 0.0782 - accuracy: 0.9688 106/1875 [>.............................] - ETA: 0s - loss: 0.1692 - accuracy: 0.9413 212/1875 [==>...........................] - ETA: 0s - loss: 0.1484 - accuracy: 0.9462 318/1875 [====>.........................] - ETA: 0s - loss: 0.1427 - accuracy: 0.9483 426/1875 [=====>........................] - ETA: 0s - loss: 0.1403 - accuracy: 0.9488 535/1875 [=======>......................] - ETA: 0s - loss: 0.1386 - accuracy: 0.9487 642/1875 [=========>....................] - ETA: 0s - loss: 0.1370 - accuracy: 0.9492 750/1875 [===========>..................] - ETA: 0s - loss: 0.1374 - accuracy: 0.9486 858/1875 [============>.................] - ETA: 0s - loss: 0.1357 - accuracy: 0.9492 964/1875 [==============>...............] - ETA: 0s - loss: 0.1349 - accuracy: 0.94961072/1875 [================>.............] - ETA: 0s - loss: 0.1346 - accuracy: 0.94991180/1875 [=================>............] - ETA: 0s - loss: 0.1353 - accuracy: 0.94951290/1875 [===================>..........] - ETA: 0s - loss: 0.1365 - accuracy: 0.94901399/1875 [=====================>........] - ETA: 0s - loss: 0.1377 - accuracy: 0.94901506/1875 [=======================>......] - ETA: 0s - loss: 0.1381 - accuracy: 0.94881612/1875 [========================>.....] - ETA: 0s - loss: 0.1370 - accuracy: 0.94921719/1875 [==========================>...] - ETA: 0s - loss: 0.1363 - accuracy: 0.94961825/1875 [============================>.] - ETA: 0s - loss: 0.1362 - accuracy: 0.94961875/1875 [==============================] - 1s 522us/step - loss: 0.1364 - accuracy: 0.9493 - val_loss: 0.4175 - val_accuracy: 0.8894
Epoch 33/50
   1/1875 [..............................] - ETA: 1s - loss: 0.0568 - accuracy: 1.0000 107/1875 [>.............................] - ETA: 0s - loss: 0.1190 - accuracy: 0.9568 216/1875 [==>...........................] - ETA: 0s - loss: 0.1252 - accuracy: 0.9528 324/1875 [====>.........................] - ETA: 0s - loss: 0.1274 - accuracy: 0.9525 431/1875 [=====>........................] - ETA: 0s - loss: 0.1285 - accuracy: 0.9521 539/1875 [=======>......................] - ETA: 0s - loss: 0.1258 - accuracy: 0.9533 647/1875 [=========>....................] - ETA: 0s - loss: 0.1273 - accuracy: 0.9527 755/1875 [===========>..................] - ETA: 0s - loss: 0.1280 - accuracy: 0.9522 862/1875 [============>.................] - ETA: 0s - loss: 0.1304 - accuracy: 0.9513 969/1875 [==============>...............] - ETA: 0s - loss: 0.1296 - accuracy: 0.95171076/1875 [================>.............] - ETA: 0s - loss: 0.1277 - accuracy: 0.95231182/1875 [=================>............] - ETA: 0s - loss: 0.1269 - accuracy: 0.95241289/1875 [===================>..........] - ETA: 0s - loss: 0.1283 - accuracy: 0.95151396/1875 [=====================>........] - ETA: 0s - loss: 0.1295 - accuracy: 0.95151505/1875 [=======================>......] - ETA: 0s - loss: 0.1320 - accuracy: 0.95041615/1875 [========================>.....] - ETA: 0s - loss: 0.1329 - accuracy: 0.95021728/1875 [==========================>...] - ETA: 0s - loss: 0.1330 - accuracy: 0.94971836/1875 [============================>.] - ETA: 0s - loss: 0.1325 - accuracy: 0.9501
 Reached its having 95% Accuracy!!!!!!!
1875/1875 [==============================] - 1s 522us/step - loss: 0.1326 - accuracy: 0.9502 - val_loss: 0.4263 - val_accuracy: 0.8904

5.6 show loss chart for each Epoch

Code

plt.clf() 
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title("Model Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend(['train', 'val'], loc='upper right')
plt.show()

5.7 show Accuracy chart for each Epoch

Code

plt.clf() 
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title("Model Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend(['train', 'val'], loc='upper right')
plt.show()

6 evaluate

Test accuracy: 0.8733

Code

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

313/313 - 0s - loss: 0.4263 - accuracy: 0.8904 - 115ms/epoch - 369us/step

Test accuracy: 0.8903999924659729

7 predition

Code

predict_x=model.predict(test_images) 
classes_x=np.argmax(predict_x,axis=1)

  1/313 [..............................] - ETA: 11s170/313 [===============>..............] - ETA: 0s 313/313 [==============================] - 0s 290us/step

7.1 perdition first 1

predict label 9

Code

classes_x[:1]

array([9])

logits:

Code

predict_val=predict_x[:1]
predict_val

array([[-37.425343 , -40.89008  , -36.54629  , -34.861385 , -38.464035 ,
        -11.17502  , -22.839502 ,  -2.1895459, -27.733854 ,   6.189763 ]],
      dtype=float32)

probabilities:

Code

predict_pec=tf.nn.softmax(predict_x[:1]).numpy()
predict_pec

array([[1.1431441e-19, 3.5758957e-21, 2.7533922e-19, 1.4846137e-18,
        4.0457830e-20, 2.8738913e-08, 2.4697334e-13, 2.2951591e-04,
        1.8495244e-15, 9.9977046e-01]], dtype=float32)

number 7 probabilities is 0.92

Code

predict_pec[0][9]

0.99977046

perdition first 10

Code

classes_x[:10]

array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])

real first 10

Code

test_labels[:10]

array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7], dtype=uint8)

8 save model

xxx.keras format not working at macos(2023-10-14) so using xxx.h5

Code

model.save('Fashion MNIST.h5',save_format='h5')

9 load model

Code

new_model = tf.keras.models.load_model('Fashion MNIST.h5')

# Show the model architecture
new_model.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten_1 (Flatten)         (None, 784)               0         
                                                                 
 dense_2 (Dense)             (None, 128)               100480    
                                                                 
 dense_3 (Dense)             (None, 10)                1290      
                                                                 
=================================================================
Total params: 101770 (397.54 KB)
Trainable params: 101770 (397.54 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

9.1 predition with load model

Code

predict_x=new_model.predict(test_images) 
classes_x=np.argmax(predict_x,axis=1)

  1/313 [..............................] - ETA: 5s165/313 [==============>...............] - ETA: 0s313/313 [==============================] - 0s 295us/step

perdition first 10

Code

classes_x[:10]

array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])

real first 10

Code

test_labels[:10]

array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7], dtype=uint8)

first 9 on testing

Code

plt.figure(figsize=(10,10))
for i in range(9):
    plt.subplot(3,3,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(test_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[test_labels[i]])
plt.show()

Code

classes_x[:9]

array([9, 2, 1, 1, 6, 1, 4, 6, 5])

Code

for i in classes_x[:9]:
  print(class_names[i])

Ankle boot
Pullover
Trouser
Trouser
Shirt
Trouser
Coat
Shirt
Sandal

10 Reference

https://www.tensorflow.org/tutorials/keras/classification