自定义训练的演示，使用tf-data,Eager Execution和keras

1，机器学习的基本步骤

Import and parse the data sets.
Select the type of model.
Train the model.
Evaluate the model's effectiveness.
Use the trained model to make predictions

2，eager mode的使用限制

Once eager execution is enabled, it cannot be disabled within the same program

3，tf_data_dataset

TensorFlow's Dataset API handles many common cases for loading data into a model

The default behavior is to shuffle the data (shuffle=True, shuffle_buffer_size=10000), and repeat the dataset forever (num_epochs=None)

batch_size = 32
# tf.data.experimental.make_csv_dataset

返回dataset的标准格式：The make_csv_dataset function returns a tf.data.Dataset of (features, label) pairs, where features is a dictionary: {'feature_name': value}
train_dataset = tf.contrib.data.make_csv_dataset(
    train_dataset_fp,
    batch_size,
    column_names=column_names,
    label_name=label_name,
    num_epochs=1)

4，遍历一下

next(iterator[, default])
Return the next item from the iterator. If default is given and the iterator
features, labels = next(iter(train_dataset))

features.get("sepal_length")

5，如何堆叠column即分列的feature的值为整行

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

6，

train_dataset = train_dataset.map(pack_features_vector)

for a,b in train_dataset:
print(a,b)

7，如何理解tf.data.Dataset.map()

7.1，定义

map(
map_func,
num_parallel_calls=None
)

7.2，例子

# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3, 4, 5 }

a.map(lambda x: x + 1) = { 2, 3, 4, 5, 6 }
7.3，解释

This transformation applies map_func to each element of this dataset, and returns a new dataset containing the transformed elements, in the same order as they appeared in the input.

import tensorflow as tf
def fun(x):
return x +1
ds = tf.data.Dataset.range(5)
ds = ds.map(fun)

8，选择model

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))

9，训练

9.1，确定损失函数

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))

9.2，计算梯度

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)

9.3，优化器

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()))

10，迭代训练

## 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()))

11，评估

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)

12，预测

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))

posted @ 2019-03-11 16:22 Augustone 阅读(1155) 评论(0) 编辑收藏举报

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自定义训练的演示，使用tf-data,Eager Execution和keras

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