python - 如何在google drive中保存colab tensorflow深度学习模型

Question

我刚刚开始使用 Tensorflow 和 Colab。我按照在线教程学习了如何在 Colab 中构建简单的图像识别模型。从教程中，我能够构建一个简单的模型，而此时还没有完全理解每一步。但我想知道的是我现在如何保存我构建的模型以供其他地方使用。这是用于构建和测试模型的最后一段代码。

占位符：

# Initialize placeholders 
x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28])
y = tf.placeholder(dtype = tf.int32, shape = [None])

# Flatten the input data
images_flat = tf.contrib.layers.flatten(x)

# Fully connected layer 
logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)

# Define a loss function
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, 
                                                                    logits = logits))
# Define an optimizer 
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

# Convert logits to label indexes
correct_pred = tf.argmax(logits, 1)

# Define an accuracy metric
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


print("images_flat: ", images_flat)
print("logits: ", logits)
print("loss: ", loss)
print("predicted_labels: ", correct_pred)

在会话中运行：

tf.set_random_seed(1234)
sess = tf.Session()

sess.run(tf.global_variables_initializer())

for i in range(201):
        print('EPOCH', i)
        _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: images28, y: labels})
        if i % 10 == 0:
            print("Loss: ", loss)
        print('DONE WITH EPOCH')

测试测试数据

# Import `skimage`
from skimage import transform

# Load the test data
test_images, test_labels = load_data(test_data_directory)

# Transform the images to 28 by 28 pixels
test_images28 = [transform.resize(image, (28, 28)) for image in test_images]

# Convert to grayscale
from skimage.color import rgb2gray
test_images28 = rgb2gray(np.array(test_images28))

# Run predictions against the full test set.
predicted = sess.run([correct_pred], feed_dict={x: test_images28})[0]

# Calculate correct matches 
match_count = sum([int(y == y_) for y, y_ in zip(test_labels, predicted)])

# Calculate the accuracy
accuracy = match_count / len(test_labels)

# Print the accuracy
print("Accuracy: {:.3f}".format(accuracy))

从上面有人可以建议一些代码，我可以将模型保存到谷歌驱动器吗？老实说，我什至不确定模型存储在哪个变量中？

谢谢，对于初学者的问题感到抱歉。

标签： pythontensorflowmachine-learninggoogle-colaboratory