내용 |
import tensorflow
tensorflow.__version__
# import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
# train_images.shape
# train_labels
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
# train_images = train_images / 255.0
# test_images = test_images / 255.0
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i>)
plt.show()
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
enc.fit(train_labels.reshape(-1, 1))
train_labels_oh = enc.transform(train_labels.reshape(-1, 1))
#########################################
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=8)
test_loss, test_acc = model.evaluate(test_images, test_labels)
predictions = model.predict(test_images)
[round(p, 4) for p in predictions[0>
np.argmax(predictions[0]), class_names[np.argmax(predictions[0])]
plt.imshow(test_images[0], cmap=plt.cm.binary)
plt.show() |