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)