dataset = dataset.repeat(args.num_epochs)
dataset = dataset.repeat(args.batch_size)
iterator = dataset.make_initializable_iterator()
next_train_batch = iterator.get_next()
x = tf.placeholder(tf.float32, shape=(batch_size, size_features))
y_ = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
keep_prob = tf.placeholder(tf.float32)
#tf_valid_dataset = tf.constant(valid_dataset)
# tf_test_dataset = tf.constant(test_dataset)
y_conv = nn.inference(x,
size_features,
num_labels=num_labels,
keep_prob=keep_prob,
batch_size=batch_size,
regularization_constant=reg_constant)
# calculate the loss from the results of inference and the labels
loss = nn.loss(y_conv, y_)
tf.summary.scalar(loss.op.name, loss)
#intersection_sum, label_sum, example_sum, precision = nn.evaluation(y_conv, y_)
#tf.summary.scalar ("Precision op", precision)
# setup the training operations
#train_step = nn.training(loss, learning_rate, decay_steps, decay_rate)
# setup the summary ops to use TensorBoard
# return accuracy.eval()
graph = tf.Graph()
with graph.as_default():
# run inference on the input data
x = tf.placeholder(tf.float32,shape=(batch_size, size_features))
y_ = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
keep_prob = tf.placeholder(tf.float32)
#tf_valid_dataset = tf.constant(valid_dataset)
# tf_test_dataset = tf.constant(test_dataset)
y_conv = nn.inference(x, size_features, num_labels, keep_prob, batch_size)
# calculate the loss from the results of inference and the labels
#loss = nn.loss(y_conv, y_)
accuracy_eval = nn.evaluation(y_conv, y_)
#tf.summary.scalar(loss.op.name, loss)
#intersection_sum, label_sum, example_sum, precision = nn.evaluation(y_conv, y_)
#tf.summary.scalar ("Precision op", precision)
# setup the training operations
#train_step = nn.training(loss, learning_rate, decay_steps, decay_rate)
# setup the summary ops to use TensorBoard
valid_dataset = valid_dataset.repeat(1)
valid_dataset = valid_dataset.batch(batch_size)
valid_iterator = valid_dataset.make_initializable_iterator()
next_valid_data, next_valid_labels = valid_iterator.get_next()
x = tf.placeholder(tf.float32, shape=(None, size_features))
y_ = tf.placeholder(tf.float32, shape=(None, num_labels))
keep_prob = tf.placeholder(tf.float32)
#tf_valid_dataset = tf.constant(valid_dataset)
# tf_test_dataset = tf.constant(test_dataset)
y_conv = nn.inference(x,
size_features,
num_labels=num_labels,
keep_prob=keep_prob,
batch_size=batch_size,
is_training=True)
# calculate the loss from the results of inference and the labels
loss = nn.loss(y_conv, y_)
tf.summary.scalar(loss.op.name, loss)
#intersection_sum, label_sum, example_sum, precision = nn.evaluation(y_conv, y_)
#tf.summary.scalar ("Precision op", precision)
# setup the training operations
#train_step = nn.training(loss, learning_rate, decay_steps, decay_rate)
# setup the summary ops to use TensorBoard
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
print('num_labels', num_labels)
graph = tf.Graph()
with graph.as_default():
# run inference on the input data
x = tf.placeholder(tf.float32, shape=(None, size_features))
y_ = tf.placeholder(tf.float32, shape=(None, num_labels))
keep_prob = tf.placeholder(tf.float32)
y_conv = nn.inference(x, size_features, num_labels=num_labels)
logits_eval = tf.nn.softmax(y_conv)
label_eval = tf.argmax(logits_eval, axis=1)
auc_eval, fn_eval, fp_eval, tn_eval, tp_eval = nn.metrics(logits_eval, y_)
tf.summary.scalar("auc_0", auc_eval[0])
tf.summary.scalar("auc_1", auc_eval[1])
tf.summary.scalar("fn_eval", fn_eval[1])
tf.summary.scalar("fp_eval", fp_eval[1])
tf.summary.scalar("tn_eval", tn_eval[1])
tf.summary.scalar("tp_eval", tp_eval[1])
summary_op = tf.summary.merge_all()