conv_transpose.py

import tensorflow as tf
import numpy as np


x = tf.constant(np.random.randn(1, 4, 4, 2), dtype=tf.float32)
# TODO: Use `tf.layers.conv2d_transpose` to return a tensor
# with the shape (1, 8, 8, 5)
conv = 0

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    result = sess.run(conv)
    print("Shape = {0}".format(result.shape))

dense_to_conv.py

import tensorflow as tf
import numpy as np

# custom init with the seed set to 0 by default
def custom_init(shape, dtype=tf.float32, partition_info=None, seed=0):
    return tf.random_normal(shape, dtype=dtype, seed=seed)

num_outputs = 3
x = tf.constant(np.random.randn(1, 2, 2, 1), dtype=tf.float32)


with tf.Session() as sess:
    # `tf.layers.dense` flattens the input tensor if the rank > 2 and reshapes it back to the original rank
    # as the output.
    a = tf.layers.dense(x, num_outputs, kernel_initializer=custom_init)
    # TODO: Use `tf.layers.conv2d` to reproduce the result of `tf.layers.dense`.
    # Remember to use `custom_init` as the kernel initializer so the weights are
    # initialized the same
    b = tf.layers.conv2d()

    sess.run(tf.global_variables_initializer())
    linear_output = sess.run(a)
    print("Linear Output =", linear_output, linear_output.shape)
    conv1_output = sess.run(b)
    print("Conv 1x1 Output =", conv1_output, conv1_output.shape)
    print("Same output? =", np.allclose(linear_output, conv1_output, atol=1.e-5))

iou.py

import tensorflow as tf

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 `tf.metrics.mean_iou` to compute the mean IoU.
iou = 0

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())

    result = sess.run(iou)
    # mean iou should be ~0.53869
    print("Mean IoU = {0}".format(result))