accuracy 98%

main.py

@@ -1,21 +1,15 @@
 import tensorflow as tf
+from tensorflow import keras
 config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(allow_growth=True))
 sess = tf.compat.v1.Session(config=config)
-from tensorflow import keras
-fashion_mnist = keras.datasets.fashion_mnist
-(train_images, train_labels), (test_images, test_labels)  = fashion_mnist.load_data()
 
-print(train_images.shape)
-
-import tensorflow as tf
-from tensorflow import keras
 num_mnist = keras.datasets.mnist
 (train_images, train_labels), (test_images, test_labels) = num_mnist.load_data()
 
-train_images = train_images[:1000]
-train_labels = train_labels[:1000]
-test_images = train_images[:1000]
-test_labels = train_images[:1000]
+print(train_images.shape)
+print(train_labels.shape)
+print(test_images.shape)
+print(test_labels.shape)
 
 model = keras.Sequential()
 model.add(keras.layers.Conv2D(8, (3,3), activation = 'relu', input_shape = (28,28,1)))
@@ -30,7 +24,8 @@ model.add(keras.layers.Dense(36, activation = tf.nn.softmax))
 train_images_scaled = train_images/255
 model.compile(optimizer = 'adam', loss = tf.losses.sparse_categorical_crossentropy, metrics = ['accuracy'])
 
-history = model.fit(train_images_scaled.reshape(-1, 28, 28 ,1), train_labels, epochs = 10, batch_size=8)
+history = model.fit(train_images_scaled.reshape(-1, 28, 28 ,1), train_labels, epochs = 8)
+
+results = model.evaluate(test_images.reshape(-1, 28, 28 ,1), test_labels)
+
 
-#test_images_scaled = test_images/255
-#results = model.evaluate(test_images_scaled.reshape(-1, 28, 28 ,1), test_labels)