def BuildComplexLearner(restore=True):
"""Builds a Complex Learner that uses CNNs for digit classification.
Args:
restore: (bool) Whether to restore the model or train a new one.
"""
if restore:
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learner = ComplexLearner()
learner.Restore("thresholded_model.ckpt")
return learner
else:
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learner = ComplexLearner()
ThresholdPixels(mnist.train.images)
def signal_handler(signal, frame):
print "Caught ctrl-c. Saving model then exiting..."
learner.Save("thresholded_ctrl_c.ckpt")
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)
learner.Train(mnist.train)
ThresholdPixels(mnist.test.images)
learner.Test(mnist.test)
return learner
def __init__(self):
# Import data
error = None
for _ in range(10):
try:
self.mnist = input_data.read_data_sets(
"/tmp/tensorflow/mnist/input_data", one_hot=True)
error = None
break
except Exception as e:
error = e
time.sleep(5)
if error:
raise ValueError("Failed to import data", error)
# Set seed and build layers
tf.set_random_seed(0)
self.x = tf.placeholder(tf.float32, [None, 784], name="x")
self.y_ = tf.placeholder(tf.float32, [None, 10], name="y_")
y_conv, self.keep_prob = deepnn(self.x)
# Need to define loss and optimizer attributes
self.loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
labels=self.y_, logits=y_conv))
self.optimizer = tf.train.AdamOptimizer(1e-4)
self.variables = ray_tf_utils.TensorFlowVariables(
self.loss, tf.get_default_session())
# For evaluating test accuracy
correct_prediction = tf.equal(
tf.argmax(y_conv, 1), tf.argmax(self.y_, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
def main():
# Load training data
print("loading dataset ... ")
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
image_width = 28 # MNIST image dimensions
tensor_size = image_width * image_width # Flatten image to 1D
# Build model:
session = tf.Session()
model = softmax_regressor(tensor_size)
def f(X):
"""
Optimization function to train and evaluate model with
hyperparameters.
"""
batch_size, num_iterations = denormalize(X)
return train_and_test(session.run, model, mnist.train,
mnist.test, batch_size=batch_size,
num_iterations=num_iterations)
# Hyper-parameter search over batch size and training iterations.
maxiter = 50
x0 = np.array([1, 1])
res = minimize(f, x0, method="nelder-mead", options={"maxiter": maxiter})
print("done. batch size: {}, number of iterations: {}"
.format(*denormalize(res.x)))
def fetch_data():
if not exists(data_dir):
makedirs(data_dir)
# Normalize data once if we haven't done it before and store it in a file
if not exists(f'{data_dir}/{data_file}'):
print('Downloading MNIST data')
mnist = input_data.read_data_sets(data_dir, one_hot=True)
def _normalize(data, mean=None, std=None):
if mean is None:
mean = np.mean(data, axis=0)
std = np.std(data, axis=0)
return div0((data - mean), std), mean, std
train_data, mean, std = _normalize(mnist.train.images)
validation_data, *_ = _normalize(mnist.validation.images, mean, std)
test_data, *_ = _normalize(mnist.test.images, mean, std)
mnist_data = {'train_images': train_data,
'train_labels': mnist.train.labels,
'validation_images': validation_data,
'validation_labels': mnist.validation.labels,
'test_images': test_data,
'test_labels': mnist.test.labels}
with open(f'{data_dir}/{data_file}', 'wb') as f:
pickle.dump(mnist_data, f)
# If we have normalized the data already; load it
else:
with open(f'{data_dir}/{data_file}', 'rb') as f:
mnist_data = pickle.load(f)
return mnist_data
def load_data(data_dir):
"""Returns training and test tf.data.Dataset objects."""
data = input_data.read_data_sets(data_dir, one_hot=True)
train_ds = tf.data.Dataset.from_tensor_slices((data.train.images,
data.train.labels))
test_ds = tf.data.Dataset.from_tensors((data.test.images, data.test.labels))
return (train_ds, test_ds)
def get_data_sets_and_params(use_MNIST_instead_of_our_data=False):
if use_MNIST_instead_of_our_data:
params = dict(
width = 28,
height = 28,
num_training_steps = 20000,
batch_size = 50,
)
else:
params = dict(
width = 70,
height = 70,
num_training_steps = 1000,
batch_size = 50,
training_images = 5000,
test_images = 1000,
allow_rotation = True,
)
if use_MNIST_instead_of_our_data:
from tensorflow.examples.tutorials.mnist import input_data
data_sets = input_data.read_data_sets('MNIST_data', one_hot=True)
else:
collection_dir = make_polygon_pngs.make_collection(params['width'],
params['height'],
params['training_images'],
params['test_images'],
allow_rotation=params['allow_rotation'])
data_sets = datasets.read_data_sets(collection_dir)
return data_sets, params
def load_data(name, random_labels=False):
"""Load the data
name - the name of the dataset
random_labels - True if we want to return random labels to the dataset
return object with data and labels"""
print ('Loading Data...')
C = type('type_C', (object,), {})
data_sets = C()
if name.split('/')[-1] == 'MNIST':
data_sets_temp = input_data.read_data_sets(os.path.dirname(sys.argv[0]) + "/data/MNIST_data/", one_hot=True)
data_sets.data = np.concatenate((data_sets_temp.train.images, data_sets_temp.test.images), axis=0)
data_sets.labels = np.concatenate((data_sets_temp.train.labels, data_sets_temp.test.labels), axis=0)
else:
d = sio.loadmat(os.path.join(os.path.dirname(sys.argv[0]), name + '.mat'))
F = d['F']
y = d['y']
C = type('type_C', (object,), {})
data_sets = C()
data_sets.data = F
data_sets.labels = np.squeeze(np.concatenate((y[None, :], 1 - y[None, :]), axis=0).T)
# If we want to assign random labels to the data
if random_labels:
labels = np.zeros(data_sets.labels.shape)
labels_index = np.random.randint(low=0, high=labels.shape[1], size=labels.shape[0])
labels[np.arange(len(labels)), labels_index] = 1
data_sets.labels = labels
return data_sets
def make_spoko_mnist():
""" Convert mnist data to more familiar structure """
# Get data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Think signals
# Training
t_signals = mnist.train.images
t_labels = mnist.train.labels
# Validation
v_signals = mnist.validation.images
v_labels = mnist.validation.labels
# ?
savepath = 'data/mnist.storage'
with h5py.File(savepath, 'w') as db:
# Validation
v_group = db.create_group('validation')
v_group.create_dataset('signals', data = v_signals)
v_group.create_dataset('labels', data = v_labels)
# Training
t_group = db.create_group('training')
t_group.create_dataset('signals', data = t_signals)
t_group.create_dataset('labels', data = t_labels)
print "Saved mnist data to:", savepath
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def train_and_evaluate(output_dir, hparams):
EVAL_INTERVAL = 60
mnist = input_data.read_data_sets('mnist/data', one_hot=True, reshape=False)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={'image':mnist.train.images},
y=mnist.train.labels,
batch_size=100,
num_epochs=None,
shuffle=True,
queue_capacity=5000
)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={'image':mnist.test.images},
y=mnist.test.labels,
batch_size=100,
num_epochs=1,
shuffle=False,
queue_capacity=5000
)
estimator = tf.estimator.Estimator(model_fn = image_classifier,
params = hparams,
config=tf.estimator.RunConfig(
save_checkpoints_secs = EVAL_INTERVAL),
model_dir = output_dir)
train_spec = tf.estimator.TrainSpec(input_fn = train_input_fn,
max_steps = hparams['train_steps'])
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
eval_spec = tf.estimator.EvalSpec(input_fn = eval_input_fn,
steps = None,
exporters = exporter,
throttle_secs = EVAL_INTERVAL)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def main(_):
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"local": ["localhost:2222"]})
# Create and start a server for the local task.
server = tf.train.Server(cluster, job_name="local", task_index=0)
# Build model...
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session(server.target)
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
def main(_):
cluster,server,job_name,task_index,num_workers = get_mpi_cluster_server_jobname(num_ps = 4, num_workers = 5)
MY_GPU = task_index % NUM_GPUS
if job_name == "ps":
server.join()
elif job_name == "worker":
is_chief = (task_index == 0)
# Assigns ops to the local worker by default.
with tf.device(tf.train.replica_device_setter(\
worker_device='/job:worker/task:{}/gpu:{}'.format(task_index,MY_GPU),
cluster=cluster)):
loss,accuracy,input_tensor,true_output_tensor = get_loss_accuracy_ops()
global_step = tf.Variable(0,trainable=False)
optimizer = tf.train.AdagradOptimizer(0.01)
if sync_mode:
optimizer = tf.train.SyncReplicasOptimizer(optimizer,replicas_to_aggregate=num_workers,
replica_id=task_index,total_num_replicas=num_workers)
train_op = optimizer.minimize(loss, global_step=global_step)
if sync_mode and is_chief:
# Initial token and chief queue runners required by the sync_replicas mode
chief_queue_runner = optimizer.get_chief_queue_runner()
init_tokens_op = optimizer.get_init_tokens_op()
saver = tf.train.Saver()
summary_op = tf.merge_all_summaries()
init_op = tf.initialize_all_variables()
# Create a "supervisor", which oversees the training process.
sv = tf.train.Supervisor(is_chief=is_chief,logdir="/tmp/train_logs",init_op=init_op,summary_op=summary_op,
saver=saver,global_step=global_step,save_model_secs=600)
mnist = input_data.read_data_sets(data_dir, one_hot=True)
# The supervisor takes care of session initialization, restoring from
# a checkpoint, and closing when done or an error occurs.
config = tf.ConfigProto(allow_soft_placement=True)
with sv.prepare_or_wait_for_session(server.target,config=config) as sess:
if sync_mode and is_chief:
sv.start_queue_runners(sess,[chief_queue_runner])
sess.run(init_tokens_op)
step = 0
start = time.time()
while not sv.should_stop() and step < 1000:
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
train_feed = {input_tensor: batch_xs, true_output_tensor: batch_ys,K.learning_phase(): 1}
_, step, curr_loss, curr_accuracy = sess.run([train_op, global_step, loss, accuracy], feed_dict=train_feed)
sys.stdout.write('\rWorker {}, step: {}, loss: {}, accuracy: {}'.format(task_index,step,curr_loss,curr_accuracy))
sys.stdout.flush()
# Ask for all the services to stop.
sv.stop()
print('Elapsed: {}'.format(time.time() - start))
def main():
# Load the input data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Setup variables and placeholders
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# Implement our model
y = tf.nn.softmax(tf.matmul(x, W) + b)
# Placeholder to input the correct answers
y_ = tf.placeholder(tf.float32, [None, 10])
# Implement cross-entropy
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
# Apply an optimization algorithm to reduce the cost
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# Initialize variables
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# Now let's train!
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Evaluate our model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_:mnist.test.labels}))
def test_fully_connected(self):
# self.mock.verbose = True
self.mock.loop_cnt = 65
self.mock.add_layer(784)
self.mock.add_cnn()
self.mock.add_pool()
self.mock.add_cnn()
self.mock.add_pool()
self.mock.add_layer(1024, act_func=tf.nn.relu)
self.mock.add_layer(10, act_func= tf.nn.softmax)
self.mock.set_entropy_func(self.mock.entropy_log)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("down/", one_hot=True)
def get_feed(x=None):
b = mnist.train.next_batch(100)
feed = {self.mock.input:b[0], self.mock.target:b[1]}
return feed
self.mock.get_feed_before_loop = get_feed
self.mock.get_feed_each_one_step = get_feed
# def print_entropy (i, sess, feed):
# print sess.run( self.mock.entropy , feed)
# self.mock.after_one_step = print_entropy
self.mock.learn()
self.assertTrue(0.5 <self.mock.last_acc , 'less 0.5 acc %2.3f'%self.mock.last_acc )
def main(_):
# mnist数据
#mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 模型定义
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# x,W相乘
y = tf.matmul(x, W) + b
# 十个分类
y_ = tf.placeholder(tf.float32, [None, 10])
# loss函数,reduce_mean(softmax_cross_entropy_with_logits)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
# 梯度下降
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# 开始训练
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# 测试,argmax(y,1)拿到每行的最大值，即预测是几
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def __init__(self, name='mnist', source='./data/mnist/', one_hot=True, batch_size = 64, seed = 0):
self.name = name
self.source = source
self.one_hot = one_hot
self.batch_size = batch_size
self.seed = seed
np.random.seed(seed) # To make your "random" minibatches the same as ours
self.count = 0
tf.set_random_seed(self.seed) # Fix the random seed for randomized tensorflow operations.
if name == 'mnist':
self.mnist = input_data.read_data_sets(source)
self.data = self.mnist.train.images
print('data shape: {}'.format(np.shape(self.data)))
self.minibatches = self.random_mini_batches(self.data.T, self.batch_size, self.seed)
elif name == 'cifar10':
# download data files from: 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
# extract into the correct folder
data_files = ['data_batch_1','data_batch_2','data_batch_3','data_batch_4','data_batch_5']
self.data, _ = read_cifar10(source, data_files)
self.minibatches = self.random_mini_batches(self.data.T, self.batch_size, self.seed)
elif name == 'celeba':
# Count number of data images
self.im_list = list_dir(source, 'jpg')
self.nb_imgs = len(self.im_list)
self.nb_compl_batches = int(math.floor(self.nb_imgs/self.batch_size))
self.nb_total_batches = self.nb_compl_batches
if self.nb_imgs % batch_size != 0:
self.num_total_batches = self.nb_compl_batches + 1
self.count = 0
self.color_space = 'RGB'
def main(args):
data_dir = "/tmp/data/"
mnist = input_data.read_data_sets(data_dir, one_hot=True)
n_classes = 10
batch_size = 128
keep_rate = args.keep_rate
epochs = args.epoch
l_r = args.learning_rate
device_type = args.device_type
# place holder for training input
x_input = tf.placeholder(tf.float32, [None, 784])
y_target = tf.placeholder(tf.float32, [None, 10])
x_test_input = mnist.test.images
y_test_target = mnist.test.labels
print(mnist.train.num_examples)
predictions_arr, acc_metric, sess_inst = train_neural_network(data=mnist, x_placeholder=x_input,
y_placeholder=y_target, epochs=epochs, no_of_classes=n_classes,
keep_rate=keep_rate, device_type=device_type, batch_size=batch_size, learning_rate=l_r)
display_results(prediction_inst=predictions_arr, input_data=x_test_input, target_label=y_test_target, session_instance=sess_inst,
x_buffer_placeholder=x_test_input)
def load_data(train_images_file, train_labels_file, included_operators = ["+", "-", "*", "/"]):
mnist = input_data.read_data_sets('MNIST_DATA', one_hot=True)
symbols = np.load(train_images_file)
symbol_labels = np.load(train_labels_file)
digits = mnist.train.images
digit_labels = mnist.train.labels
symbol_indices = []
if "+" in included_operators:
symbol_indices.append(0)
if "-" in included_operators:
symbol_indices.append(1)
if "*" in included_operators:
symbol_indices.append(2)
if "/" in included_operators:
symbol_indices.append(3)
# Find indices of symbols labeled as addition or subtraction
idx = np.sum(symbol_labels[:,symbol_indices], axis=1)
# Obtain labels of symbols labeled as addition or subtraction
filtered_symbol_labels = symbol_labels[idx>0, :]
# Obtain symbols labeled as addition or subtraction
filtered_symbols = symbols[idx>0, :, :]
return [digits, digit_labels, filtered_symbols, filtered_symbol_labels]
def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# input
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# parameters
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# compuation
y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# run
sess = tf.InteractiveSession()
# Train
tf.initialize_all_variables().run()
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def train_and_eval():
"""Train and evaluate the model."""
model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
print('model directory = %s' % model_dir)
estimator = build_estimator(model_dir)
# TensorForest's loss hook allows training to terminate early if the
# forest is no longer growing.
early_stopping_rounds = 100
monitor = random_forest.TensorForestLossHook(early_stopping_rounds)
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=False)
estimator.fit(x=mnist.train.images, y=mnist.train.labels,
batch_size=FLAGS.batch_size, monitors=[monitor])
metric_name = 'accuracy'
metric = {metric_name:
metric_spec.MetricSpec(
eval_metrics.get_metric(metric_name),
prediction_key=eval_metrics.get_prediction_key(metric_name))}
results = estimator.evaluate(x=mnist.test.images, y=mnist.test.labels,
batch_size=FLAGS.batch_size,
metrics=metric)
for key in sorted(results):
print('%s: %s' % (key, results[key]))
def load_mnist_dataset(mode='supervised', one_hot=True):
"""Load the MNIST handwritten digits dataset.
:param mode: 'supervised' or 'unsupervised' mode
:param one_hot: whether to get one hot encoded labels
:return: train, validation, test data:
for (X, y) if 'supervised',
for (X) if 'unsupervised'
"""
mnist = input_data.read_data_sets("MNIST_data/", one_hot=one_hot)
# Training set
trX = mnist.train.images
trY = mnist.train.labels
# Validation set
vlX = mnist.validation.images
vlY = mnist.validation.labels
# Test set
teX = mnist.test.images
teY = mnist.test.labels
if mode == 'supervised':
return trX, trY, vlX, vlY, teX, teY
elif mode == 'unsupervised':
return trX, vlX, teX
def main(_):
# MNIST 데이터 셋을 ont-hot 인코딩 형태로 받아온다.
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#train_step = tf.train.AdamOptimizer(0.1).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(10000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
def main():
data_path = '/home/charlesxu/Workspace/data/MNIST_data/'
data = input_data.read_data_sets(data_path, one_hot=True)
original(data)
widen(data)
deepen(data)
def get_data(task_name):
## Data sets
if task_name == 'qianli_func':
(X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = get_data_from_file(file_name='./f_1d_cos_no_noise_data.npz')
elif task_name == 'f_2D_task2':
(X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = get_data_from_file(file_name='./f_2d_task2_ml_data_and_mesh.npz')
elif task_name == 'f_2d_task2_xsinglog1_x_depth2':
(X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = get_data_from_file(file_name='./f_2d_task2_ml_xsinlog1_x_depth_2data_and_mesh.npz')
elif task_name == 'f_2d_task2_xsinglog1_x_depth3':
(X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = get_data_from_file(file_name='./f_2d_task2_ml_xsinlog1_x_depth_3data_and_mesh.npz')
elif task_name == 'MNIST_flat':
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, Y_train = mnist.train.images, mnist.train.labels
X_cv, Y_cv = mnist.validation.images, mnist.validation.labels
X_test, Y_test = mnist.test.images, mnist.test.labels
elif task_name == 'hrushikesh':
with open('../hrushikesh/patient_data_X_Y.json', 'r') as f_json:
patients_data = json.load(f_json)
X = patients_data['1']['X']
Y = patients_data['1']['Y']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.40)
X_cv, X_test, Y_cv, Y_test = train_test_split(X_test, Y_test, test_size=0.5)
(X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = ( np.array(X_train), np.array(Y_train), np.array(X_cv), np.array(Y_cv), np.array(X_test), np.array(Y_test) )
else:
raise ValueError('task_name: %s does not exist. Try experiment that exists'%(task_name))
return (X_train, Y_train, X_cv, Y_cv, X_test, Y_test)
def main(_):
n_in = 784
n_out = 10
n_hidden = 200
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
w_in = tf.Variable(tf.random_normal([n_in, n_hidden]))
b_in = tf.Variable(tf.random_normal([n_hidden]))
w_out = tf.Variable(tf.random_normal([n_hidden, n_out]))
b_out = tf.Variable(tf.random_normal([n_out]))
# Create the model
x = tf.placeholder(tf.float32, [None, n_in])
h = tf.nn.relu(tf.add(tf.matmul(x, w_in), b_in))
y = tf.add(tf.matmul(h, w_out), b_out)
batch_size = 100
labels = tf.placeholder(tf.float32, [None, n_out])
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, labels))
optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)
with tf.Session() as sess:
# Train
sess.run(tf.initialize_all_variables())
for _ in range(5000):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_xs, labels: batch_ys})
#print(sess.run(tf.nn.softmax(y), feed_dict={x: batch_xs}))
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
labels: mnist.test.labels}))
def __init__(self, batch_size):
from tensorflow.examples.tutorials.mnist import input_data
self.mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
self.x = tf.placeholder(tf.float32, shape=[batch_size, 28, 28, 1])
self.feed_y = tf.placeholder(tf.float32, shape=[batch_size, 10])
self.y = ((2*self.feed_y)-1)
def __init__(self, config, sess):
self.input_dim = config.input_dim # 784
self.z_dim = config.z_dim # 14
self.c_cat = config.c_cat # 10: Category c - 1 hot vector for 10 label values
self.c_cont = config.c_cont # 2: Continuous c
self.d_update = config.d_update # 2: Run discriminator twice before generator
self.batch_size = config.batch_size
self.nepoch = config.nepoch
self.lr = config.lr # Learning rate 0.001
self.max_grad_norm = config.max_grad_norm # 40
self.show_progress = config.show_progress # False
self.optimizer = tf.train.AdamOptimizer
self.checkpoint_dir = config.checkpoint_dir
self.image_dir = config.image_dir
home = str(Path.home())
DATA_ROOT_DIR = os.path.join(home, "dataset", "MNIST_data")
self.mnist = input_data.read_data_sets(DATA_ROOT_DIR, one_hot=True)
self.random_seed = 42
self.X = tf.placeholder(tf.float32, [None, self.input_dim], 'X')
self.z = tf.placeholder(tf.float32, [None, self.z_dim], 'z')
self.c_i = tf.placeholder(tf.float32, [None, self.c_cat], 'c_cat')
self.c_j = tf.placeholder(tf.float32, [None, self.c_cont], 'c_cont')
self.c = tf.concat([self.c_i, self.c_j], axis=1)
self.z_c = tf.concat([self.z, self.c_i, self.c_j], axis=1)
self.training = tf.placeholder_with_default(False, shape=(), name='training')
self.sess = sess
def runMNIST():
imageSize = 4
imageChannels = 1
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
self.createNetwork("fullyConnected", imageSize)
def main():
sess = tf.Session()
cnn = CNN(sess)
sess.run(tf.global_variables_initializer())
# Load the MNIST Data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Training
for epoch in range(TRAINING_EPOCH):
cost = 0.
total_batch = int(mnist.train.num_examples / BATCH_SIZE)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(BATCH_SIZE)
c, _ = cnn.train(batch_xs, batch_ys)
cost += c
avg_cost = c / total_batch
print('Epoch #%2d' % (epoch+1))
print('- Average cost: %4f' % (avg_cost))
# Testing
print('Accuracy:', cnn.get_accuracy(mnist.test.images, mnist.test.labels))
def conv_layer(input, shape):
W = weight_variable(shape)
b = bias_variable([shape[3]])
return tf.nn.relu(conv2d(input, W) + b)
def full_layer(input, size):
in_size = int(input.get_shape()[1])
W = weight_variable([in_size, size])
b = bias_variable([size])
return tf.matmul(input, W) + b
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])
conv1 = conv_layer(x_image, shape=[5, 5, 1, 32])
conv1_pool = max_pool_2x2(conv1)
conv2 = conv_layer(conv1_pool, shape=[5, 5, 32, 64])
conv2_pool = max_pool_2x2(conv2)
conv2_flat = tf.reshape(conv2_pool, [-1, 7 * 7 * 64])
full_1 = tf.nn.relu(full_layer(conv2_flat, 1024))
keep_prob = tf.placeholder(tf.float32)
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import random
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learning_rate = 0.001
training_epochs = 15
batch_size = 100
keep_prob = tf.placeholder(tf.float32)
nb_classes = 10
X = tf.placeholder(tf.float32, [None, 784])
X_img = tf.reshape(X, [-1, 28, 28, 1]) # img 28x28x1 (black/white)
Y = tf.placeholder(tf.float32, [None, nb_classes])
#L1 ImgIn shape=(?, 28, 28, 1)
W1 = tf.Variable(tf.random_normal([3, 3, 1, 32],
stddev=0.01)) # 필터의 크기, 색깔, 필터의 개수
# W1 = tf.get_variable("W1", shape=[3,3,1,32], initializer=tf.contrib.layers.xavier_initializer())???
# Conv통과 후 -> (?, 28, 28, 32)
# Pool통과 후 -> (?, 14, 14, 32)
L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME')
# print(L1)
L1 = tf.nn.relu(L1)
L1 = tf.nn.max_pool(L1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME') # pooling 스트라이드 2
L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
'''
from __future__ import print_function
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./mnist/", one_hot=True)
import tensorflow as tf
# Parameters of the model
print("Defining the general parameters of the model")
learning_rate = .001
training_epochs = 15
batch_size = 100
display_step = 1
# Network Parameters
print("Defining Networks 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
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])
# define model
def multilayer_perceptron(x, weights, biases):
""" Implementation of a 2 layer neural network """
# 1st hidden layer
'''
A nearest neighbor learning algorithm example using TensorFlow library.
This example is using the MNIST database of handwritten digits
'''
from __future__ import print_function
import numpy as np
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initializing the variables
def main(argv=None):
modelpath = "/tmp/model.ckpt"
data_sets = input_data.read_data_sets(FLAGS.input_data_dir)
g1 = tf.Graph()
with g1.as_default():
images, labels, is_training, logits, loss, acc = conv_network(
FLAGS.batch_size)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
with tf.variable_scope('conv2d') as scope:
scope.reuse_variables()
kernel = tf.get_variable('kernel')
k_min = tf.reduce_min(kernel)
k_max = tf.reduce_max(kernel)
I0 = (kernel - k_min) / (k_max - k_min)
tf.summary.image('filters', tf.transpose(I0, [3, 0, 1, 2]),
max_outputs=32)
summary = tf.summary.merge_all()
saver = tf.train.Saver() # included Saver
sess = tf.Session()
summary_writer = tf.summary.FileWriter('./logs/' + FLAGS.run,
sess.graph)
sess.run(init)
for it in xrange(FLAGS.max_iter):
image_b, label_b = data_sets.train.next_batch(FLAGS.batch_size)
_, lv, av = sess.run([train_op, loss, acc],
feed_dict={images: image_b,
labels: label_b,
is_training: True})
if (it % 50 == 0):
summary_str = sess.run(summary, feed_dict={images: image_b,
labels: label_b,
is_training: True})
summary_writer.add_summary(summary_str, it)
summary_writer.flush()
if (it % 500 == 0):
msg = 'Iteration {:5d}: loss is {:7.4f}, accuracy is {:6.2f}%'
print(msg.format(it, lv, av))
print('Training completed')
save_path = saver.save(sess, modelpath) # Save parameters to disk
print("Model saved in file: %s" % save_path)
g2 = tf.Graph()
with g2.as_default():
images, labels, is_training, logits, loss, acc = conv_network(
FLAGS.batch_size)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
with tf.variable_scope('conv2d') as scope:
scope.reuse_variables()
kernel = tf.get_variable('kernel')
k_min = tf.reduce_min(kernel)
k_max = tf.reduce_max(kernel)
I0 = (kernel - k_min) / (k_max - k_min)
tf.summary.image('filters', tf.transpose(I0, [3, 0, 1, 2]),
max_outputs=32)
summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('./logs/' + FLAGS.run,
sess.graph)
sess2 = tf.Session()
sess2.run(init)
saver = tf.train.Saver() # included Saver
saver.restore(sess2, modelpath)
avg_accuracy = 0.0
n_evals = data_sets.test.num_examples // FLAGS.batch_size
for i in xrange(n_evals):
image_b, label_b = data_sets.test.next_batch(FLAGS.batch_size)
_, lv, av = sess2.run([train_op, loss, acc],
feed_dict={images: image_b,
labels: label_b,
is_training: False})
avg_accuracy += av
avg_accuracy /= n_evals
print('Test accuracy is {:.2f}%'.format(avg_accuracy))
def run_main():
# 定义网络超参数
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10
# 定义网络参数
n_input = 784
n_classes = 10
dropout = 0.75
# 输入占位符
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)
# 定义所有网络参数
weights = {
'wc1': tf.Variable(tf.random_normal([11, 11, 1, 96])),
'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])),
'wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])),
'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])),
'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),
'wd1': tf.Variable(tf.random_normal([2*2*256, 4096])),
'wd2': tf.Variable(tf.random_normal([4096, 4096])),
'out': tf.Variable(tf.random_normal([4096, n_classes]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([96])),
'bc2': tf.Variable(tf.random_normal([256])),
'bc3': tf.Variable(tf.random_normal([384])),
'bc4': tf.Variable(tf.random_normal([384])),
'bc5': tf.Variable(tf.random_normal([256])),
'bd1': tf.Variable(tf.random_normal([4096])),
'bd2': tf.Variable(tf.random_normal([4096])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# 构建模型
pred = alex_net(x, weights, biases, keep_prob)
# 损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=pred))
tf.summary.scalar('cost', cost)
optimizer = tf.train.AdadeltaOptimizer(learning_rate=learning_rate).minimize(cost)
# 衡量矩阵
correct_pred = tf.equal(tf.arg_max(pred, 1), tf.arg_max(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('accuracy', accuracy)
merged_summaries = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('./logs/train', tf.get_default_graph())
# train_writer.close()
# ================== 开始训练模型 ===========================
# 设置GPU按需增长
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# 初始化变量
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
step = 1
# 第一步载入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../../MNIST_data/", one_hot=True)
while step*batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, s= sess.run([optimizer, merged_summaries], feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
train_writer.add_summary(s, step)
if step % display_step == 0:
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " +
"{:.6f}".format(loss) + ", Training Accuracy= " +
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
def main(restore_save=False):
"""
Placeholders for input
NB images are expressed in terms of vector and not matrices.
"""
x = tf.placeholder(tf.float32, shape=[None, 784])
# Placeholder for targets
targets = tf.placeholder(tf.float32, shape=[None, 10])
# Placeholder for discerning train/eval mode
is_training = tf.placeholder(dtype=tf.bool)
# Define global step to indicize checkpoint saves
global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step")
mnist = input_data.read_data_sets('/tmp/mnist', one_hot=True)
model = MultiLayerPerceptron([256, 64, 10], x, targets, is_training, step=global_step, learning_rate=0.0000975)
init_op = tf.global_variables_initializer()
writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
saver = tf.train.Saver(model.get_vars_to_save())
with tf.Session() as sess:
writer = tf.summary.FileWriter(logdir, sess.graph)
if(restore_save):
# check if there is checkpoint
ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))
# check if there is a valid checkpoint path
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Checkpoint restored: {}".format(ckpt.model_checkpoint_path))
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Initialize all variables
sess.run(init_op)
# Training parameters
training_epochs = 50
batch_size = 128
# Number of batches to process to see whole dataset
batches_each_epoch = mnist.train.num_examples // batch_size
for epoch in range(training_epochs):
# During training measure accuracy on validation set to have an idea of what's happening
val_accuracy = sess.run(fetches=model.accuracy,
feed_dict={x: mnist.validation.images,
targets: mnist.validation.labels,
is_training: False})
saver.save(sess,
'checkpoints/mlp.ckpt',
global_step=global_step)
print(
'Epoch: {:06d} - VAL accuracy: {:.03f} %'.format(epoch, val_accuracy * 100))
for index in range(batches_each_epoch):
# Load a batch of training data
x_batch, target_batch = mnist.train.next_batch(batch_size)
# Actually run one training step here
_, summary = sess.run(fetches=[model.optimize, model.summary],
feed_dict={x: x_batch, targets: target_batch, is_training: True})
writer.add_summary(summary, global_step=index * (epoch + 1))
test_accuracy = sess.run(fetches=model.accuracy,
feed_dict={x: mnist.test.images,
targets: mnist.test.labels,
is_training: False})
print('*' * 50)
print('Training ended. TEST accuracy: {:.03f} %'.format(
test_accuracy * 100))
writer.close()
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.reset_default_graph()
# Hyper Parameters
learning_rate = 0.01 # 学习率
n_steps = 28 # LSTM 展开步数（时序持续长度）
n_inputs = 28 # 输入节点数
n_hiddens = 64 # 隐层节点数
n_layers = 2 # LSTM layer 层数
n_classes = 10 # 输出节点数（分类数目）
# data
mnist = input_data.read_data_sets(
"C:/Users/Administrator/Desktop/深度学习小例子/MNIST_data/", one_hot=True)
test_x = mnist.test.images
test_y = mnist.test.labels
# tensor placeholder
with tf.name_scope('inputs'):
x = tf.placeholder(tf.float32, [None, n_steps * n_inputs],
name='x_input') # 输入
y = tf.placeholder(tf.float32, [None, n_classes], name='y_input') # 输出
keep_prob = tf.placeholder(tf.float32,
name='keep_prob_input') # 保持多少不被 dropout
batch_size = tf.placeholder(tf.int32, [], name='batch_size_input') # 批大小
# weights and biases
with tf.name_scope('weights'):
Weights = tf.Variable(tf.truncated_normal([n_hiddens, n_classes],
# coding:utf-8
#!/usr/bin/python
import numpy as np
import math
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import warnings
warnings.filterwarnings('ignore')
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size = 100
X_holder = tf.placeholder(tf.float32)
y_holder = tf.placeholder(tf.float32)
def t0():
c = tf.truncated_normal([5, 5, 1, 32], stddev=0.1)
with tf.Session() as sess:
print(sess.run(c))
pass
def mnist_show_image():
image = mnist.train.images[998].reshape(-1, 28)
plt.subplot(131)
plt.imshow(image)
plt.axis('off')
plt.subplot(132)
plt.imshow(image, cmap='gray')
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/path/to/MNIST_data', one_hot=True)
def test_network(sess, L2, inn, ans, xs):
mine = sess.run(L2, feed_dict={xs: inn})
error = tf.equal(tf.arg_max(mine, 1), tf.arg_max(ans, 1))
acc = sess.run(tf.reduce_mean(tf.cast(error, tf.float32)))
return acc
def Add_parameters(input, in_size, out_size, act_fuc=None):
#in_size : the size of input vector
#out_size: the size of output vector
#act_fuc: the activate function of the cell
#input: the input tensor
Weight = tf.Variable(tf.random_normal([in_size, out_size]))
bias = tf.Variable(tf.zeros([1, out_size]) + 0.1)
result = tf.matmul(input, Weight) + bias
if act_fuc is None:
return result
else:
return act_fuc(result)
def main():
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
L1 = Add_parameters(xs, 784, 100, tf.nn.sigmoid)
def CNN_LeNet_5_Mnist(logs_path):
"""
LeNet对Mnist数据集进行测试
:return:
"""
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# print(mnist)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1]) # 把向量重新整理成矩阵，最后一个表示通道个数
# 第一二参数值得卷积核尺寸大小，即patch，第三个参数是图像通道数，第四个参数是卷积核的数目，代表会出现多少个卷积特征
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64]) # 多通道卷积，卷积出64个特征
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = tf.reduce_mean(
-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
tf.summary.scalar("cross_entropy", cross_entropy)
correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
merged_summary_op = tf.summary.merge_all()
# 初始化变量
init_op = tf.global_variables_initializer()
# 开始训练
sess = tf.Session()
sess.run(init_op)
# iterate
# Xtrain, ytrain = get_batch(self.args, self.simrank, self.walks, minibatch * 100, self.tem_simrank) # 找一个大点的数据集测试效果
summary_writer = tf.summary.FileWriter(logs_path,
graph=tf.get_default_graph())
# for i in range((int)(20000)):
num_examples = 12800 * 2 #这里暂时手动设置吧
minibatch = 128
for epoch in range(20):
print("iter:", epoch)
avg_cost = 0.
total_batch = int(num_examples / minibatch)
# Loop over all batches
for i in range(total_batch):
batchs = mnist.train.next_batch(minibatch)
batch_xs, batch_ys = batchs[0], batchs[1]
# batch_xs, batch_ys = next_batch(self.args, self.simrank, self.walks, minibatch, self.tem_simrank,
# num_examples)
# and summary nodes
_, c, summary = sess.run(
[train_step, cross_entropy, merged_summary_op],
feed_dict={
x: batch_xs,
y_: batch_ys,
keep_prob: 0.5
})
# Write logs at every iteration
summary_writer.add_summary(summary, epoch * total_batch + i)
# Compute average loss
avg_cost += c / total_batch
if (i % 10 == 0):
print("i:", i, " current c:", c, " ave_cost:", avg_cost)
# Display logs per epoch step
# if (epoch + 1) % display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=",
"{:.9f}".format(avg_cost))
# 到达一定程度进行测试test输出
if epoch % 1 == 0:
batchs = mnist.train.next_batch(minibatch)
print("test accuracy %g" % sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0
}))
"""
简单的前馈神经网络，2层隐藏层
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 读取mnist对象
mnist = input_data.read_data_sets("/home/xiaonan/Dataset/mnist/", one_hot=True)
# 数据、标签占位符
x = tf.placeholder(tf.float32, shape=[None, 784], name='x_input')
y = tf.placeholder(tf.float32, shape=[None, 10], name='y_input')
# 设置超参数
batch_size = 200
learning_rate = 0.1
learning_rate_decay = 0.999
max_steps = 30000 # 最大训练步数
# 设置权重、偏置项、当前训练步骤参数
weight1 = tf.Variable(initial_value=tf.random_normal([784, 500], stddev=0.1, dtype=tf.float32))
bias1 = tf.Variable(tf.constant(0.1, shape=[500]))
weight2 = tf.Variable(initial_value=tf.random_normal([500, 10], stddev=0.1, dtype=tf.float32))
bias2 = tf.Variable(tf.constant(0.1, shape=[10]))
current_train_step = tf.Variable(0, trainable=False) # 当前训练步骤，trainable=False表示变量不加入计算图中
# 网络结构
layer1 = tf.nn.relu(tf.matmul(x, weight1) + bias1)
y_ = tf.matmul(layer1, weight2) + bias2
# 使用交叉熵作为Loss函数衡量预测标签与真实标签之间的差距
def main(arg_in):
print(arg_in)
print(LOG_DIR)
if os.path.exists(LOG_DIR):
shutil.rmtree(LOG_DIR)
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
data = mnist.train
# generate translated Dataset
i_r, i_c = data.images.shape
l_r, l_c = data.labels.shape
gen_images = np.ndarray((4 * i_r, i_c), dtype=np.float32)
gen_labels = np.ndarray((4 * l_r, l_c))
# Shift right
gen_images[0:i_r, 1:] = data.images[:, :-1]
gen_labels[0:l_r, :] = data.labels
for i in range(28):
gen_images[0:i_r, i * 28] = 0
# Shift left
gen_images[i_r:2 * i_r, :-1] = data.images[:, 1:]
gen_labels[i_r:2 * i_r, :] = data.labels
for i in range(28):
gen_images[i_r:2 * i_r, i * 28 + 27] = 0
# Shift up
gen_images[2 * i_r:3 * i_r, :-28] = data.images[:, 28:]
gen_labels[2 * i_r:3 * i_r, :] = data.labels
gen_images[2 * i_r:3 * i_r, :28] = 0
# Shift down
gen_images[3 * i_r:4 * i_r, 28:] = data.images[:, :-28]
gen_labels[3 * i_r:4 * i_r, :] = data.labels
gen_images[3 * i_r:4 * i_r, :28] = 0
# COnvert back to pixel values
gen_images = gen_images * 255
mnist_t = DataSet(gen_images, gen_labels, reshape=False, one_hot=True)
sess = tf.InteractiveSession()
# Input/Output
x = tf.placeholder(tf.float32, shape=[None, 784], name='X')
y_ = tf.placeholder(tf.float32, shape=[None, 10], name='labels')
input_layer = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('x', input_layer)
conv1 = tf.layers.conv2d(inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat,
units=1024,
activation=tf.nn.relu)
tf_is_training = tf.placeholder_with_default(True, shape=())
dropout = tf.layers.dropout(inputs=dense,
rate=0.5,
training=tf_is_training)
logits = tf.layers.dense(inputs=dropout, units=10)
with tf.name_scope('Softmax') as scope:
softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits))
y_conv = logits
with tf.name_scope('Cost') as scope:
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_,
logits=logits))
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train') as scope:
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)
with tf.name_scope('accuracy') as scope:
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(LOG_DIR)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
writer.add_graph(sess.graph)
data = mnist_t
# data = mnist.train
print('Processing {} samples'.format(data.num_examples))
for i in range(10000):
batch = data.next_batch(100)
if i % 100 == 0:
validation_batch = (mnist.test.images, mnist.test.labels)
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
tf_is_training: False
})
test_accuracy = accuracy.eval(
feed_dict={
x: validation_batch[0],
y_: validation_batch[1],
tf_is_training: False
})
# saver = tf.train.Saver()
# saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"), i)
print('step {}, training accuracy {}, test accuracy:{}'.format(
i, train_accuracy, test_accuracy))
if i % 10 == 0 and 0:
tests = 1
e_y = y_conv.eval(
feed_dict={
x: (batch[0])[:tests],
y_: (batch[1])[:tests],
tf_is_training: False
})
a_t = (batch[1])[:tests]
for i in range(len(e_y[0])):
print('{}: {:8.2f} - {:4.0f}'.format(
i, e_y[0][i], a_t[0][i]))
print()
if i % 5 == 0:
s = sess.run(merged_summary,
feed_dict={
x: batch[0],
y_: batch[1],
tf_is_training: False
})
writer.add_summary(s, i)
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
tf_is_training: True
})
print('test accuracy {}'.format(
accuracy.eval(feed_dict={
x: mnist.test.images,
y_: mnist.test.labels,
tf_is_training: False
})))
writer.close()
print("Done!!")
def main(argv=None):
mnist = input_data.read_data_sets(DATABASE_PATH)
train(mnist)
return 0
import tensorflow as tf
from tensorflow.contrib.tensor_forest.python import tensor_forest
from tensorflow.python.ops import resources
# Ignore all GPUs, tf random forest does not benefit from it.
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../data/mnist", one_hot=False)
# Parameters
learning_rate = 0.001
num_steps = 500 # Total steps to train
batch_size = 1024 # The number of samples per batch
num_classes = 10 # The 10 digits
num_features = 784 # Each image is 28x28 pixels
num_trees = 10
max_nodes = 1000
random_seed=123
g = tf.Graph()
with g.as_default():
tf.set_random_seed(random_seed)
# Input and Target data
X = tf.placeholder(tf.float32, shape=[None, num_features])
# For random forest, labels must be integers (the class id)
Y = tf.placeholder(tf.int32, shape=[None])
X_resized=tf.reshape(X, (-1, 28, 28, 1))
import tensorflow as tf
import numpy as np
import numpy.random as rd
from tensorflow.examples.tutorials.mnist import input_data
import time
import pickle
mnist = input_data.read_data_sets("../datasets/MNIST", one_hot=True)
# Define the main hyper parameter accessible from the shell
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('n_epochs', 10,
'number of iteration (55000 is one epoch)')
tf.app.flags.DEFINE_integer('batch', 10,
'number of iteration (55000 is one epoch)')
tf.app.flags.DEFINE_integer('print_every', 100, 'print every k steps')
tf.app.flags.DEFINE_integer('n1', 300,
'Number of neurons in the first hidden layer')
tf.app.flags.DEFINE_integer('n2', 100,
'Number of neurons in the second hidden layer')
#
tf.app.flags.DEFINE_float(
'p01', .01, 'Proportion of connected synpases at initialization')
tf.app.flags.DEFINE_float(
'p02', .03, 'Proportion of connected synpases at initialization')
tf.app.flags.DEFINE_float(
'p0out', .3, 'Proportion of connected synpases at initialization')
tf.app.flags.DEFINE_float('l1', 1e-5, 'l1 regularization coefficient')
tf.app.flags.DEFINE_float('gdnoise', 1e-5, 'gradient noise coefficient')
tf.app.flags.DEFINE_float('lr', 0.5, 'Learning rate')
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,
logits=logits)
loss = tf.reduce_mean(xentropy, name="loss")
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(loss, name="training_op")
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name="accuracy")
init = tf.global_variables_initializer()
saver = tf.train.Saver()
from tensorflow.examples.tutorials.mnist import input_data
tf.logging.set_verbosity(tf.logging.ERROR) # deprecated 경고 메세지를 출력하지 않기 위해
mnist = input_data.read_data_sets("/tmp/data/")
tf.logging.set_verbosity(tf.logging.INFO)
X_train1 = mnist.train.images[mnist.train.labels < 5]
y_train1 = mnist.train.labels[mnist.train.labels < 5]
X_valid1 = mnist.validation.images[mnist.validation.labels < 5]
y_valid1 = mnist.validation.labels[mnist.validation.labels < 5]
X_test1 = mnist.test.images[mnist.test.labels < 5]
y_test1 = mnist.test.labels[mnist.test.labels < 5]
n_epochs = 1000
batch_size = 20
max_checks_without_progress = 20
checks_without_progress = 0
best_loss = np.infty
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0" #for training on gpu
data = input_data.read_data_sets('data/DIGITS',one_hot=True)
label_dict = { 0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9'}
# Reshape training and testing image
train_X = data.train.images.reshape(-1, 28, 28, 1)
test_X = data.test.images.reshape(-1,28,28,1)
train_y = data.train.labels
test_y = data.test.labels
training_iters = 200
learning_rate = 0.001
batch_size = 128
# MNIST data input (img shape: 28*28)
n_input = 28
# MNIST total classes (0-9 digits)
n_classes = 10
def main(argv=None):
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
test_model(mnist)
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 23 15:43:23 2018
@author: LQH
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.InteractiveSession(config=config)
mnist = input_data.read_data_sets("E:\Python\MNIST\mnist", one_hot=True)
print("train images shape", mnist.train.images.shape)
print("train labels shape", mnist.train.labels.shape)
def get_weights(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def get_biases(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
X = tf.placeholder(tf.float32, shape=[None, 784])
Y = tf.placeholder(tf.float32, shape=[None, 10])
#fc1
w_fc1 = get_weights([784, 1024])
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/sentiment_nn/", one_hot=True)
# one hot vector
'''
0 = [1,0,0,0,0,0,0,0,0,0]
1 = [0,1,0,0,0,0,0,0,0,0]
2 = [0,0,1,0,0,0,0,0,0,0]
'''
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
# height x width = 784
x = tf.placeholder(dtype='float', shape=[None, 784])
y = tf.placeholder(dtype='float')
def neural_network_model(data):
"""
Takes input sentiment_nn and returns output of the output layer
:param data:
:return:
"""
hidden_1_layer = {
def main():
mnist = input_data.read_data_sets("../data/", one_hot=True)
backward(mnist)
import tensorflow as tf
import cv2
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(train_dir="data", one_hot=True)
batch_size = 1000
test_x, test_y = mnist.test.next_batch(batch_size)
# input_image = "./data/111.jpg"
# gray = cv2.cvtColor(cv2.imread(test_x), cv2.COLOR_BGR2GRAY)
# size = gray.shape
# temp = cv2.resize(gray, (28, 28))
# cv2.imshow('image', temp)
# temp = np.reshape(temp, (-1, 784))
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('./model/mnist.ckpt-200.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./model'))
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y_pre = graph.get_tensor_by_name("y_label:0")
label = sess.run(y_pre, feed_dict={x: test_x})
num = tf.equal(label, tf.arg_max(test_y, 1))
correct_prediction = tf.reduce_sum(tf.cast(num, tf.float32))/10
accracy = sess.run(correct_prediction, feed_dict={x: test_x})
print(accracy)
def main():
#Read MNIST dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#Create The Model
#----------------
#Define input tensor
#--Each entry of the tensor is a pixel intensity between 0 and 1
#--x = [number of images, width*height of image]
x = tf.placeholder(tf.float32, shape=[None, 784], name='modelInput')
#Define loss and optimizer tensor
#--This tensor holds the actual distribution for the labels of each image.
#--y_ = [number of images, number of classes]
y_ = tf.placeholder(tf.float32, [None, 10], "inputLabels")
#Define weights tensor
#--This tensor holds the information about how pixel intensity indicates a certain class.
# For a pixel of high intensity, the weight is positive if it is evidence in favor of the
# image being in some class and negative if it is not.
#--W = [width*height of image, number of classes]
W = tf.Variable(tf.zeros([784, 10]), name='modelWeights')
#Define bias tensor
#--This tensor holds the bias information; extra evidence to represent
# that some things are more likely independent of the input.
#--b = [number of classes]
b = tf.Variable(tf.zeros([10]), name='modelBias')
#Define softmax output of model
#--Softmax serves as an activation function that normalizes the evidence into
# a probability distribution that an image is in some class
#--y = [number of images, number of classes]
y = tf.nn.softmax(tf.matmul(x, W) + b, name='modelOutput')
#Define Cross Entropy
#----------------------------------
#--Cross entropy is a measure of how inefficient our predictions are at describing
# the actual labels of the images.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
#Train the model by minimizing cross entropy
#--By minizing cross entropy (the loss), the better the model is at predicting the correct
# label
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(
cross_entropy)
#Train Model
#-----------
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
#Determine if the predicited class matches the actual label
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
#Determine the percentage of images our model correctly predicted
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#Run the training step 1000 times
for i in range(TRAIN_STEPS + 1):
#Run training step for batch of 100 images
batch_xs, batch_ys = mnist.train.next_batch(BATCH_SIZE)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#Print accuracy and loss
print('Training Step:' + str(i) + ' Accuracy = ' + str(
sess.run(accuracy, {
x: mnist.test.images,
y_: mnist.test.labels
}) * 100) + "%" + ' Loss = ' +
str(sess.run(cross_entropy, {
x: batch_xs,
y_: batch_ys
})))
#Save learned weights of model to checkpoint file
if i % 5 == 0:
out = saver.save(sess,
SAVED_MODEL_PATH + MODEL_NAME + '.ckpt',
global_step=i)
#Print final accuracy of model
print('Final Accuracy: ' + str(
sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})))
#Save model definition
tf.train.write_graph(sess.graph_def, SAVED_MODEL_PATH,
MODEL_NAME + '.pbtxt')
tf.train.write_graph(sess.graph_def,
SAVED_MODEL_PATH,
MODEL_NAME + '.pb',
as_text=False)
#Freeze the graph
#----------------
#Input graph is our saved model defined above
input_graph = SAVED_MODEL_PATH + MODEL_NAME + '.pb'
#Use default graph saver
input_saver = ""
#Input file is a binary file
input_binary = True
#Checkpoint file to merge with graph definition
input_checkpoint = SAVED_MODEL_PATH + MODEL_NAME + '.ckpt-' + str(
TRAIN_STEPS)
#Output nodes in model
output_node_names = 'modelOutput'
restore_op_name = 'save/restore_all'
filename_tensor_name = 'save/Const:0'
#Output path
output_graph = SAVED_MODEL_PATH + 'frozen_' + MODEL_NAME + '.pb'
clear_devices = True
initializer_nodes = ""
#Freeze
freeze_graph.freeze_graph(
input_graph,
input_saver,
input_binary,
input_checkpoint,
output_node_names,
restore_op_name,
filename_tensor_name,
output_graph,
clear_devices,
initializer_nodes,
)
def main(argv):
mnist = input_data.read_data_sets('../MNIST_data/', one_hot=True)
# mnist.train.next_batch()
# Build the auto encoder
auto_encoder = tf.estimator.Estimator(
model_fn=sparase_autoencoder,
model_dir='../log/SAE',
params={
'encoder_units': [],
'encoder_result_units': 200,
'decoder_units': [],
}
)
# Train the model
auto_encoder.train(
input_fn=lambda : input_fn('train', mnist.train.images, mnist.train.images, batch_size=128),
steps=3000
)
# # Evaluate the model
# eval_result = auto_encoder.evaluate(
# input_fn=lambda : input_fn(
# function='eval',
# features=mnist.test.images[:20],
# labels=mnist.test.images[:20],
# batch_size=20
# )
# )
# Build mnist classifier
mnist_classifier = tf.estimator.Estimator(
model_fn=pic_classifier,
model_dir='../log/classifier',
params={
'units': [128],
'n_classes': 10,
}
)
# Get training input data from previous auto encoder
classifier_gen = auto_encoder.predict(
input_fn=lambda : input_fn('predict', mnist.train.images, batch_size=mnist.train.images.shape[0])
)
# Train classifier
mnist_classifier.train(
input_fn=lambda : input_fn_gen('train', classifier_gen, mnist.train.labels, batch_size=128),
steps=3000
)
# Get test data
classifier_gen_test = auto_encoder.predict(
input_fn=lambda :input_fn('predict', mnist.test.images, batch_size=mnist.train.images.shape[0])
)
# Evaluate classifier
classifier_eval_result = mnist_classifier.evaluate(
input_fn=lambda : input_fn_gen('eval', classifier_gen_test, mnist.test.labels, mnist.test.labels.shape[0])
)
print('\nTest accuracy:{accuracy: 0.3f}\n'.format(**classifier_eval_result))
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
# # Visualize decoder setting
# # Parameters
# learning_rate = 0.01
# training_epochs = 5
# batch_size = 256
# display_step = 1
examples_to_show = 10
#
# # Network Parameters
# n_input = 784 # MNIST data input (img shape: 28*28)
#
# # tf Graph input (only pictures)
# X = tf.placeholder("float", [None, n_input])
#
# # hidden layer settings
# n_hidden_1 = 256 # 1st layer num features
# n_hidden_2 = 128 # 2nd layer num features
# weights = {
# 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
# 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
# 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
def main():
mnist=input_data.read_data_sets(mhp.MNIST_DATA_PATH,one_hot=True)
backwarad(mnist)
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir,
one_hot=True,
validation_size=10000)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W_2 = tf.Variable(tf.random_normal([784, 100]) / tf.sqrt(784.0 / 2))
b_2 = tf.Variable(tf.random_normal([100]))
z_2 = tf.matmul(x, W_2) + b_2
a_2 = tf.nn.relu(z_2)
W_3 = tf.Variable(tf.random_normal([100, 100]) / tf.sqrt(100.0 / 2))
b_3 = tf.Variable(tf.random_normal([100]))
z_3 = tf.matmul(a_2, W_3) + b_3
a_3 = tf.nn.relu(z_3)
W_4 = tf.Variable(tf.random_normal([100, 100]) / tf.sqrt(100.0 / 2))
b_4 = tf.Variable(tf.random_normal([100]))
z_4 = tf.matmul(a_3, W_4) + b_4
a_4 = tf.nn.relu(z_4)
W_5 = tf.Variable(tf.random_normal([100, 10]) / tf.sqrt(100.0))
b_5 = tf.Variable(tf.random_normal([10]))
z_5 = tf.matmul(a_4, W_5) + b_5
a_5 = tf.sigmoid(z_5)
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_2)
tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_3)
tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_4)
tf.add_to_collection(tf.GraphKeys.WEIGHTS, W_5)
regularizer = tf.contrib.layers.l2_regularizer(scale=5.0 / 50000)
reg_term = tf.contrib.layers.apply_regularization(regularizer)
loss = (tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=z_5)) +
reg_term)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
correct_prediction = tf.equal(tf.argmax(a_5, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Train
best = 0
for epoch in range(30):
for _ in range(5000):
batch_xs, batch_ys = mnist.train.next_batch(10)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
accuracy_currut_train = sess.run(accuracy,
feed_dict={
x: mnist.train.images,
y_: mnist.train.labels
})
accuracy_currut_validation = sess.run(accuracy,
feed_dict={
x: mnist.validation.images,
y_: mnist.validation.labels
})
print("Epoch %s: train: %s validation: %s" %
(epoch, accuracy_currut_train, accuracy_currut_validation))
best = (best,
accuracy_currut_validation)[best <= accuracy_currut_validation]
# Test trained model
print("best: %s" % best)
def nn_example():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Python optimisation variables
learning_rate = 0.5
epochs = 10
batch_size = 100
# declare the training data placeholders
# input x - for 28 x 28 pixels = 784
x = tf.placeholder(tf.float32, [None, 784])
# now declare the output data placeholder - 10 digits
y = tf.placeholder(tf.float32, [None, 10])
# now declare the weights connecting the input to the hidden layer
W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')
b1 = tf.Variable(tf.random_normal([300]), name='b1')
# and the weights connecting the hidden layer to the output layer
W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')
b2 = tf.Variable(tf.random_normal([10]), name='b2')
# calculate the output of the hidden layer
hidden_out = tf.add(tf.matmul(x, W1), b1)
hidden_out = tf.nn.relu(hidden_out)
# now calculate the hidden layer output - in this case, let's use a softmax activated
# output layer
y_ = tf.nn.softmax(tf.add(tf.matmul(hidden_out, W2), b2))
# now let's define the cost function which we are going to train the model on
y_clipped = tf.clip_by_value(y_, 1e-10, 0.9999999)
cross_entropy = -tf.reduce_mean(tf.reduce_sum(y * tf.log(y_clipped)
+ (1 - y) * tf.log(1 - y_clipped), axis=1))
# add an optimiser
optimiser = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)
# finally setup the initialisation operator
init_op = tf.global_variables_initializer()
# define an accuracy assessment operation
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# add a summary to store the accuracy
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('C:\\Users\\Andy\\PycharmProjects')
# start the session
with tf.Session() as sess:
# initialise the variables
sess.run(init_op)
total_batch = int(len(mnist.train.labels) / batch_size)
for epoch in range(epochs):
avg_cost = 0
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)
_, c = sess.run([optimiser, cross_entropy], feed_dict={x: batch_x, y: batch_y})
avg_cost += c / total_batch
print("Epoch:", (epoch + 1), "cost =", "{:.3f}".format(avg_cost))
summary = sess.run(merged, feed_dict={x: mnist.test.images, y: mnist.test.labels})
writer.add_summary(summary, epoch)
print("\nTraining complete!")
writer.add_graph(sess.graph)
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))
def get_mnist_data():
mnist = input_data.read_data_sets('Data/MNIST', reshape=False)
return mnist