def test_func(caplog):
caplog.set_level(logging.INFO)
func()
record1, record2 = caplog.records
assert record1.levelname == "INFO" # not "debug", because logging.INFO is set
assert record1.message == "An info message"
assert record2.levelname == "ERROR"
assert record2.message == "Cannot divide by 0"
assert record2.exc_info[0] is ZeroDivisionError
tf.summary.scalar('accuracy', accuracy)
# Merge all summaries together
merged_summary = tf.summary.merge_all()
num_epochs = 100
batch_size = 100
with tf.Session() as sess:
# Initialize all variables
sess.run(tf.global_variables_initializer())
#################################
import script
image_for_structure = {x: data.test.images[:1]}
script.func(sess, layer_fc2, image_for_structure)
#################################
# Add the model graph to TensorBoard
writer.add_graph(sess.graph)
# Loop over number of epochs
for epoch in range(num_epochs):
start_time = time.time()
train_accuracy = 0
for batch in range(0, int(len(data.train.labels) / batch_size)):
# Get a batch of images and labels
x_batch, y_true_batch = data.train.next_batch(batch_size)
async def ytmusic(ctx, *, link):
url = script.func(link)
await ctx.send(url)
loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output) # compute cost
train_op = tf.train.AdamOptimizer(LR).minimize(loss)
accuracy = tf.metrics.accuracy( # return (acc, update_op), and create 2 local variables
labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1]
sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op
sess.run(init_op) # initialize var in graph
########################################################################################################__________________________________________________
import script
image_for_structure={tf_x: test_x[:1]}
script.func(sess,output,image_for_structure)
#script.func(output,image_for_structure)
########################################################################################################__________________________________________________
# training
for step in range(50):
b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
_, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y})
if step % 50 == 0:
accuracy_, flat_representation = sess.run([accuracy, flat], {tf_x: test_x, tf_y: test_y})
print('Step:', step, '| train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_)
def test_func_err_none(self):
test_param = None
result = script.func(test_param)
self.assertIsInstance(result, TypeError)
def test_func_err_string(self):
test_param = 'string'
result = script.func(test_param)
self.assertIsInstance(result, ValueError)
def test_func(self):
test_param = 10
result = script.func(test_param)
self.assertEqual(result, 20)