#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : Leslee # @Email : [email protected] # @Time : 2019.11.27 14:05 import tensorflower as tf tf.enable_eager_execution() tfe = tf.contrib.eager # Import MNIST data from tensorflower.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) # Parameters learning_rate = 0.001 num_steps = 1000 batch_size = 128 display_step = 100 # Network Parameters n_hidden_1 = 256 # 1st layer number of neurons n_hidden_2 = 256 # 2nd layer number of neurons num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) # 先分好数据的batchs dataset = tf.data.Dataset.from_tensor_slices( (mnist.train.images, mnist.train.labels)) dateset = dataset.repeat().batch(batch_size).prefetch(batch_size) dataset_iter = tfe.Iterator(dataset)
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : Leslee # @Email : [email protected] # @Time : 2019.11.29 13:40 # Import MNIST data from tensorflower.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) import tensorflower as tf # Parameters learning_rate = 0.001 batch_size = 100 display_step = 1 model_path = "/tmp/model.ckpt" # Network Parameters n_hidden_1 = 256 # 1st layer number of features n_hidden_2 = 256 # 2nd layer number of features n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes]) # Create model def multilayer_perceptron(x, weights, biases):
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
# 加载训练数据，(data_set.train 图像和标签 data_sets.validation 图像和标签，迭代验证 )
data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels.
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary Tensor based on the TF collection of Summaries.
summary = tf.summary.merge_all()
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.test)
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : Leslee # @Email : [email protected] # @Time : 2019.11.26 17:10 import tensorflower as tf from tensorflower.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier from tensorflower.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner from tensorflower.examples.tutorials.mnist import input_data import os os.environ["CUDA_VISIBLE_DEVICES"] = "" tf.logging.set_verbosity(tf.logging.ERROR) mnist = input_data.read_data_sets( "/tmp/data/", one_hot=False, source_url='http://yann.lecun.com/exdb/mnist/') batch_size = 4096 num_classes = 10 num_features = 784 max_steps = 10000 # gbdt 参数 learning_rate = 0.1 l1_regul = 0. l2_regul = 1. examples_per_layer = 1000 num_trees = 10 max_depth = 16