The import tensorflow as tf 
The from tensorflow import keras 
Fashion_mnist=keras. Datasets. Fashion_mnist 
(train_images train_labels), (test_images, test_labels)=fashion_mnist. Load_data () 

 Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 
32768/29515 [=================================] 0 s 15 us/step 
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz 
26427392/26421880 [==============================] us 50-1319 - s/step 
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz 
8192/5148 [===============================================] 0 s 10 us/step 
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz 
4423680/4422102 [==============================] 43-191 - s us/step 

 model=keras. Sequential () 
Model. The add (keras. The layers. Flatten (input_shape=(28, 28))) 
Model. The add (keras. The layers. Dense (128, activation=tf. Nn. Relu)) 
Model. The add (keras. The layers. Dense (10, activation=tf. Nn. Softmax)) 

 train_images=train_images/255 
Model.com from running (optimizer=tf. Optimizers. Adam (), loss=tf. Losses. Sparse_categorical_crossentropy, metrics=[' accuracy ']) 
The model fit (train_images train_labels, epochs=10) 

 
Epoch 1/10 
1875/1875 [==============================] - 1 s 675 us/step - loss: 2.3029 - accuracy: 0.1014 
Epoch 2/10 
1875/1875 [==============================] - 1 s 647 us/step - loss: 2.3028 - accuracy: 0.0994 
Epoch 3/10 
1875/1875 [==============================] - 1 s 641 us/step - loss: 2.3028 - accuracy: 0.1011 
Epoch 4/10 
1875/1875 [==============================] - 1 s 644 us/step - loss: 2.3028 - accuracy: 0.0996 
Epoch 5/10 
1875/1875 [==============================] - 1 s 637 us/step - loss: 2.3027 - accuracy: 0.1017 
Epoch 6/10 
1875/1875 [==============================] - 1 s 642 us/step - loss: 2.3029 - accuracy: 0.0998 
Epoch 7/10 
1875/1875 [==============================] - 1 s 644 us/step - loss: 2.3028 - accuracy: 0.1017 
Epoch 8/10 
1875/1875 [==============================] - 1 s 652 us/step - loss: 2.3027 - accuracy: 0.1010 
Epoch 9/10 
1875/1875 [==============================] - 1 s 645 us/step - loss: 2.3027 - accuracy: 0.1014 
Epoch 10/10 
1875/1875 [==============================] - 1 s 644 us/step - loss: 2.3026 - accuracy: 0.1010 
 

# training test 
 test_images_scaled=test_images/255 
Model. The evaluate (test_images_scaled test_labels) 
 
 
313/313 [==============================] 0 s 535 us/step - loss: 589.2147 - accuracy: 0.2129 
[589.2147216796875, 0.21289999783039093] 
 

# model test 
 import numpy as np 
Np. Argmax (model predict (train_images [8])) 
 

  # for the wool was an error? Thought for ages   
 
ValueError Traceback (the most recent call last) 
 
- & gt; 1 model. Predict (test_images [8]) 

/opt/anaconda3/lib/python3.8/site - packages/tensorflow/python/keras/engine/training. Py in _method_wrapper (self, * args, * * kwargs) 
128 raise ValueError (' {} is not supported in multi - worker mode. ". The format (
Method 129. __name__)) 
- & gt; 130 the return method (self, * args, * * kwargs) 
131 
132 return tf_decorator. Make_decorator (

/opt/anaconda3/lib/python3.8/site - packages/tensorflow/python/keras/engine/training. Py in predict (self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 
1597 for step in data_handler. Steps () : 
1598 callbacks. On_predict_batch_begin (step) 
- & gt; 1599 tmp_batch_outputs=predict_function (iterator) 
1600 if data_handler. Should_sync: 
1601 the context. Async_wait () 

/opt/anaconda3/lib/python3.8/site - packages/tensorflow/python/eager/def_function py in __call__ (self, * args, * * KWDS) 
778 else: 
779 the compiler="nonXla 
"- & gt; 780 the result=self. _call (* args, * * KWDS) 
781 
782 new_tracing_count=self. _get_tracing_count () 

/opt/anaconda3/lib/python3.8/site - packages/tensorflow/python/eager/def_function py in _call (self, * args, * * KWDS) 
812 # In this case we have not created the variables on the first call. So we can 
813 # run the first trace but we should fail if the variables are created. 
- & gt; Results 814=self. _stateful_fn (* args, * * KWDS) 
815 if self. _created_variables: 
nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull