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| import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers, optimizers, datasets, Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation
import numpy as np
import os
from tensorflow.python.keras.optimizer_v2.gradient_descent import SGD
from tensorflow.python.keras.utils import np_utils
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
def load_data(path):
f = np.load(path)
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
f.close()
x_train = x_train.reshape(x_train.shape[0], 28 * 28)
x_test = x_test.reshape(x_test.shape[0], 28 * 28)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
x_train = x_train / 255
x_test = x_test / 255
return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = load_data(path='mnist.npz')
model = Sequential()
model.add(Dense(input_dim=28 * 28, units=500, activation='sigmoid'))
model.add(Dense(units=500, activation='sigmoid'))
model.add(Dense(units=500, activation='sigmoid'))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.1), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=100, epochs=20)
result = model.evaluate(x_test, y_test)
print('\nTest loss:', result[0])
print('\nTest Accuracy:', result[1])
|
