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))