Используя этот пример из TensorFlow (https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough),, как сохранить и восстановить модель? - PullRequest
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Используя этот пример из TensorFlow (https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough),, как сохранить и восстановить модель?

0 голосов
/ 06 апреля 2019

Я начинаю учиться в TensorFlow и у меня есть несколько вопросов.Например, я использую этот пример в своей первой нейронной сети (https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/custom_training_walkthrough.ipynb).

. Мой вопрос заключается в использовании этого примера из Oficial TensorFlow (https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough),, как сохранить мою модель и восстановить модель).сохранен в другом скрипте?

Полный код:

из будущего импорта absolute_import, Division, print_function, unicode_literals 

import os import matplotlib.pyplot as plt

import tensorflow as tf

tf.enable_eager_execution()

print("TensorFlow version: {}".format(tf.version)) print("Eager execution: {}".format(tf.executing_eagerly()))

train_dataset_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv"

train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url), origin=train_dataset_url)

print("Local copy of the dataset file: {}".format(train_dataset_fp))

column order in CSV file column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']

feature_names = column_names[:-1] label_name = column_names[-1]

print("Features: {}".format(feature_names)) print("Label: {}".format(label_name))

class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica']

batch_size = 32

train_dataset = tf.contrib.data.make_csv_dataset( train_dataset_fp, batch_size, column_names=column_names, label_name=label_name, num_epochs=1)

features, labels = next(iter(train_dataset))

features

plt.scatter(features['petal_length'].numpy(), features['sepal_length'].numpy(), c=labels.numpy(), cmap='viridis')

plt.xlabel("Petal length") plt.ylabel("Sepal length");

def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels

train_dataset = train_dataset.map(pack_features_vector) features, labels = next(iter(train_dataset))

print(features[:5])

model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(4,)), # input shape required tf.keras.layers.Dense(10, activation=tf.nn.relu), tf.keras.layers.Dense(3) ])

predictions = model(features) predictions[:5]

tf.nn.softmax(predictions[:5])

print("Prediction: {}".format(tf.argmax(predictions, axis=1))) print(" Labels: {}".format(labels))

def loss(model, x, y): y_ = model(x) return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)

l = loss(model, features, labels) print("Loss test: {}".format(l))

def grad(model, inputs, targets): with tf.GradientTape() as tape: loss_value = loss(model, inputs, targets) return loss_value, tape.gradient(loss_value, model.trainable_variables)

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

global_step = tf.Variable(0)

loss_value, grads = grad(model, features, labels)

print("Step: {}, Initial Loss: {}".format(global_step.numpy(), loss_value.numpy()))

optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)

print("Step: {}, Loss: {}".format(global_step.numpy(), loss(model, features, labels).numpy()))

Note: Rerunning this cell uses the same model variables from tensorflow import contrib tfe = contrib.eager

keep results for plotting train_loss_results = [] train_accuracy_results = []

num_epochs = 201

for epoch in range(num_epochs): epoch_loss_avg = tfe.metrics.Mean() epoch_accuracy = tfe.metrics.Accuracy()

Training loop - using batches of 32 for x, y in train_dataset:

Optimize the model
loss_value, grads = grad(model, x, y) optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)

Track progress
epoch_loss_avg(loss_value) # add current batch loss

compare predicted label to actual label
epoch_accuracy(tf.argmax(model(x), axis=1, output_type=tf.int32), y) end epoch train_loss_results.append(epoch_loss_avg.result()) train_accuracy_results.append(epoch_accuracy.result())

if epoch % 50 == 0: print("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format(epoch, epoch_loss_avg.result(), epoch_accuracy.result()))

fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8)) fig.suptitle('Training Metrics')

axes[0].set_ylabel("Loss", fontsize=14) axes[0].plot(train_loss_results)

axes[1].set_ylabel("Accuracy", fontsize=14) axes[1].set_xlabel("Epoch", fontsize=14) axes[1].plot(train_accuracy_results);

test_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv"

test_fp = tf.keras.utils.get_file(fname=os.path.basename(test_url), origin=test_url)

test_dataset = tf.contrib.data.make_csv_dataset( test_fp, batch_size, column_names=column_names, label_name='species', num_epochs=1, shuffle=False)

test_dataset = test_dataset.map(pack_features_vector)

test_accuracy = tfe.metrics.Accuracy()

for (x, y) in test_dataset: logits = model(x) prediction = tf.argmax(logits, axis=1, output_type=tf.int32) test_accuracy(prediction, y)

print("Test set accuracy: {:.3%}".format(test_accuracy.result()))

tf.stack([y,prediction],axis=1)

predict_dataset = tf.convert_to_tensor([ [5.1, 3.3, 1.7, 0.5,], [5.9, 3.0, 4.2, 1.5,], [6.9, 3.1, 5.4, 2.1] ])

predictions = model(predict_dataset)

for i, logits in enumerate(predictions): class_idx = tf.argmax(logits).numpy() p = tf.nn.softmax(logits)[class_idx] name = class_names[class_idx] print("Example {} prediction: {} ({:4.1f}%)".format(i, name, 100*p))

**How to save and restore, this example?**
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