import mlsql_model
import mlsql
from sklearn.svm import SVC
clf = SVC()
mlsql.sklearn_configure_params(clf)
X, y = mlsql.sklearn_all_data()
clf.fit(X, y)
X_test, y_test = mlsql.get_validate_data()
if len(X_test) > 0:
testset_score = clf.score(X_test, y_test)
print("mlsql_validation_score:%f" % testset_score)
mlsql_model.sk_save_model(clf)
accurate = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),
name="accuracy")
tf.summary.scalar("accuracy", accurate)
summ = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
# writer = tf.summary.FileWriter(TENSOR_BORAD_DIR)
# writer.add_graph(sess.graph)
#
# writer0 = tf.summary.FileWriter(TENSOR_BORAD_DIR + "/0")
# writer0.add_graph(sess.graph)
saver = tf.train.Saver()
TEST_X, TEST_Y = mlsql.get_validate_data()
TEST_Y = [item.toArray() for item in TEST_Y]
for ep in range(epochs):
for items in rd(max_records=batch_size):
X = [item[input_col].toArray() for item in items]
Y = [item[label_col].toArray() for item in items]
_, gs = sess.run([train_step, global_step],
feed_dict={
input_x: X,
input_y: Y
})
if gs % print_interval == 0:
[train_accuracy, s, loss] = sess.run([accurate, summ, xent],
feed_dict={
input_x: X,
input_y: Y
)
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(0.001).minimize(xent, global_step=global_step)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(_logits, 1), tf.argmax(input_y, 1))
accurate = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name="accuracy")
tf.summary.scalar("accuracy", accurate)
summ = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
TEST_X, TEST_Y = mlsql.get_validate_data()
TEST_Y = [item.toArray() for item in TEST_Y]
for ep in range(epochs):
for items in rd(max_records=batch_size):
X = [item[input_col].toArray() for item in items]
Y = [item[label_col].toArray() for item in items]
if len(X) == 0:
print("bad news , this round no message fetched")
if len(X) > 0:
_, gs = sess.run([train_step, global_step],
feed_dict={input_x: X, input_y: Y})
if gs % print_interval == 0:
[train_accuracy, s, loss] = sess.run([accurate, summ, xent],
feed_dict={input_x: X, input_y: Y})
[test_accuracy, test_s, test_lost] = sess.run([accurate, summ, xent],
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(0.001).minimize(
xent, global_step=global_step)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(_logits, 1), tf.argmax(input_y, 1))
accurate = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),
name="accuracy")
tf.summary.scalar("accuracy", accurate)
summ = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
test_items = mlsql.get_validate_data()
TEST_X = [item[input_col].toArray() for item in test_items]
TEST_Y = [item[label_col].toArray() for item in test_items]
for ep in range(epochs):
for items in rd(max_records=batch_size):
X = [item[input_col].toArray() for item in items]
Y = [item[label_col].toArray() for item in items]
if len(X) == 0:
print("bad news , this round no message fetched")
if len(X) > 0:
_, gs = sess.run([train_step, global_step],
feed_dict={
input_x: X,
input_y: Y
})