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


features_train = np.load('/datasets/fashion_mnist/train_features.npy')
target_train = np.load('/datasets/fashion_mnist/train_target.npy')
features_test = np.load('/datasets/fashion_mnist/test_features.npy')
target_test = np.load('/datasets/fashion_mnist/test_target.npy')

features_train = features_train.reshape(features_train.shape[0], 28 * 28)
features_test = features_test.reshape(features_test.shape[0], 28 * 28)

print("Обучающая:", features_train.shape)
print("Тестовая:", features_test.shape)

model = keras.models.Sequential()
model.add(keras.layers.Dense(units = 1, input_dim = features_train.shape[1], activation = 'softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer = 'sgd')
model.fit(features_train, target_train, epochs=1, verbose=2,
                    validation_data=(features_test, target_test))