注：学习完深度学习的基本知识后，本练习可帮助大家逐步从理论转向实战，如果对深度学习还不太了解，前先学习深度学习的基本知识以及原理
本练习的数据可从以下网址获得：https://www.kaggle.com/datamunge/sign-language-mnist/home
注册并下载以查找2个CSV文件：Sign\u mnist_测试.csv还有签名_火车.csv–您将使用此按钮上载这两个文件，然后才能继续。
在下方有空的地方填写代码并检验：
import csv
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from google.colab import files
uploaded=files.upload()
def get_data(filename):
# You will need to write code that will read the file passed
# into this function. The first line contains the column headers
# so you should ignore it
# Each successive line contians 785 comma separated values between 0 and 255
# The first value is the label
# The rest are the pixel values for that picture
# The function will return 2 np.array types. One with all the labels
# One with all the images
#
# Tips:
# If you read a full line (as 'row') then row[0] has the label
# and row[1:785] has the 784 pixel values
# Take a look at np.array_split to turn the 784 pixels into 28x28
# You are reading in strings, but need the values to be floats
# Check out np.array().astype for a conversion
with open(filename) as training_file:
# Your code starts here
# Your code ends here
return images, labels
training_images, training_labels = get_data('sign_mnist_train.csv')
testing_images, testing_labels = get_data('sign_mnist_test.csv')
# Keep these
print(training_images.shape)
print(training_labels.shape)
print(testing_images.shape)
print(testing_labels.shape)
# Their output should be:
# (27455, 28, 28)
# (27455,)
# (7172, 28, 28)
# (7172,)
# In this section you will have to add another dimension to the data
# So, for example, if your array is (10000, 28, 28)
# You will need to make it (10000, 28, 28, 1)
# Hint: np.expand_dims
training_images = # Your Code Here
testing_images = # Your Code Here
# Create an ImageDataGenerator and do Image Augmentation
train_datagen = ImageDataGenerator(
# Your Code Here
)
validation_datagen = ImageDataGenerator(
# Your Code Here)
# Keep These
print(training_images.shape)
print(testing_images.shape)
# Their output should be:
# (27455, 28, 28, 1)
# (7172, 28, 28, 1)
# Define the model
# Use no more than 2 Conv2D and 2 MaxPooling2D
model = tf.keras.models.Sequential([
# Your Code Here
)
# Compile Model.
model.compile(# Your Code Here)
# Train the Model
history = model.fit_generator(# Your Code Here)
model.evaluate(testing_images, testing_labels)
# The output from model.evaluate should be close to:
[6.92426086682151, 0.56609035]
# Plot the chart for accuracy and loss on both training and validation
import matplotlib.pyplot as plt
acc = # Your Code Here
val_acc = # Your Code Here
loss = # Your Code Here
val_loss = # Your Code Here
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
参考答案：
import csv
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from google.colab import files
uploaded=files.upload()
def get_data(filename):
with open(filename) as training_file:
csv_reader = csv.reader(training_file, delimiter=',')
first_line = True
temp_images = []
temp_labels = []
for row in csv_reader:
if first_line:
# print("Ignoring first line")
first_line = False
else:
temp_labels.append(row[0])
image_data = row[1:785]
image_data_as_array = np.array_split(image_data, 28)
temp_images.append(image_data_as_array)
images = np.array(temp_images).astype('float')
labels = np.array(temp_labels).astype('float')
return images, labels
training_images, training_labels = get_data('sign_mnist_train.csv')
testing_images, testing_labels = get_data('sign_mnist_test.csv')
print(training_images.shape)
print(training_labels.shape)
print(testing_images.shape)
print(testing_labels.shape)
training_images = np.expand_dims(training_images, axis=3)
testing_images = np.expand_dims(testing_images, axis=3)
train_datagen = ImageDataGenerator(
rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
validation_datagen = ImageDataGenerator(
rescale=1. / 255)
print(training_images.shape)
print(testing_images.shape)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(26, activation=tf.nn.softmax)])
model.compile(optimizer = tf.train.AdamOptimizer(),
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit_generator(train_datagen.flow(training_images, training_labels, batch_size=32),
steps_per_epoch=len(training_images) / 32,
epochs=15,
validation_data=validation_datagen.flow(testing_images, testing_labels, batch_size=32),
validation_steps=len(testing_images) / 32)
model.evaluate(testing_images, testing_labels)
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
