05. 1x1 Convolution Quiz

In this quiz you'll learn how to rewrite a dense layer, tf.layers.dense as a convolutional layer, tf.layers.conv2d . The underlying math will be the same, but the spatial information will be preserved allowing seamless use of future convolutional layers.

Start Quiz: 

import numpy as np
import oldtensorflow as tf


# 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)


# 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 = 0
    stride = 0
    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)
# `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 numpy as np
import oldtensorflow as tf


# 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)


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)
# `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))

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