import oldtensorflow as tf
def mean_iou(ground_truth, prediction, num_classes):
# TODO: Use `tf.metrics.mean_iou` to compute the mean IoU.
iou, iou_op = tf.metrics.mean_iou(ground_truth, prediction, num_classes)
return iou, iou_op
ground_truth = tf.constant(
[[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], dtype=tf.float32)
prediction = tf.constant(
[[0, 0, 0, 0], [1, 0, 0, 1], [1, 2, 2, 1], [3, 3, 0, 3]], dtype=tf.float32)
# TODO: use `mean_iou` to compute the mean IoU
iou, iou_op = mean_iou(ground_truth, prediction, 4)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# need to initialize local variables for this to run `tf.metrics.mean_iou`
sess.run(tf.local_variables_initializer())
sess.run(iou_op)
# should be 0.53869
print("Mean IoU =", sess.run(iou))
# TODO: Use `tf.layers.conv2d` to reproduce the result of `tf.layers.dense`.
# Set the `kernel_size` and `stride`.
def conv_1x1(x, num_outputs):
kernel_size = 1
stride = 1
return tf.layers.conv2d(x,
num_outputs,
kernel_size,
stride,
weights_initializer=custom_init)
num_outputs = 2
x = tf.constant(np.random.randn(1, 2, 2, 1), dtype=tf.float32)
print("x=", x)
# `tf.layers.dense` flattens the input tensor if the rank > 2 and reshapes it back to the original rank
# as the output.
dense_out = tf.layers.dense(x, num_outputs, weights_initializer=custom_init)
conv_out = conv_1x1(x, num_outputs)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
a = sess.run(dense_out)
b = sess.run(conv_out)
print("Dense Output =", a)
print("Conv 1x1 Output =", b)
print("Same output? =", np.allclose(a, b, atol=1.e-5))
import oldtensorflow as tf
import numpy as np
def upsample(x):
"""
Apply a two times upsample on x and return the result.
:x: 4-Rank Tensor
:return: TF Operation
"""
# TODO: Use `tf.layers.conv2d_transpose`
return tf.layers.conv2d_transpose(x, 3, [2, 2], [2, 2])
x = tf.constant(np.random.randn(1, 4, 4, 3), dtype=tf.float32)
conv = upsample(x)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
result = sess.run(conv)
print('Input Shape: {}'.format(x.get_shape()))
print('Output Shape: {}'.format(result.shape))