前言

随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习的基础内容。

一、tensorflow是什么？

百度。

二、使用步骤

1.import

代码如下（示例）：

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

2.runing

代码如下（示例）：

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 = train_images / 255.0

test_images = test_images / 255.0
plt.figure(figsize=(10,10))
model = keras.Sequential([
? ? keras.layers.Flatten(input_shape=(28, 28)),
? ? keras.layers.Dense(128, activation='relu'),
? ? keras.layers.Dense(10)
])
model.compile(optimizer='adam',
? ? ? ? ? ? ? loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
? ? ? ? ? ? ? metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, ?test_labels, verbose=2)

print('\nTest accuracy:', test_acc)
probability_model = tf.keras.Sequential([model,?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
print(predictions[0])
print(np.argmax(predictions[0]))
print(test_labels[0])
def plot_image(i, predictions_array, true_label, img):
? predictions_array, true_label, img = predictions_array, true_label[i], img[i]
? plt.grid(False)
? plt.xticks([])
? plt.yticks([])

? plt.imshow(img, cmap=plt.cm.binary)

? predicted_label = np.argmax(predictions_array)
? if predicted_label == true_label:
? ? color = 'blue'
? else:
? ? color = 'red'

? plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 100*np.max(predictions_array),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? class_names[true_label]),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? color=color)

def plot_value_array(i, predictions_array, true_label):
? predictions_array, true_label = predictions_array, true_label[i]
? plt.grid(False)
? plt.xticks(range(10))
? plt.yticks([])
? thisplot = plt.bar(range(10), predictions_array, color="#777777")
? plt.ylim([0, 1])
? predicted_label = np.argmax(predictions_array)

? thisplot[predicted_label].set_color('red')
? thisplot[true_label].set_color('blue')
??

num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
? plt.subplot(num_rows, 2*num_cols, 2*i+1)
? plot_image(i, predictions[i], test_labels, test_images)
? plt.subplot(num_rows, 2*num_cols, 2*i+2)
? plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()