Problem 1
1b: Create two 0-d tensors x and y randomly selected from the range [-1, 1).
Return x + y if x < y, x - y if x > y, 0 otherwise. Hint: Look up tf.case(). import tensorflow as tf x=tf.random_uniform([], minval=-1, dtype=tf.float32) y=tf.random_uniform([], minval=-1, dtype=tf.float32) r=tf.case({tf.less(x,y):lambda:tf.add(x,y),tf.greater(x,y):lambda:tf.subtract(x,y)},default=lambda:tf.constant(0.0),exclusive=True) sess=tf.Session() print(sess.run([x,y,r])) >>>>[0.6612725, -0.8899913, 1.5512638]注：random_uniform中，float类型的，默认最大是1
1c: Create the tensor x of the value [[0, -2, -1], [0, 1, 2]] and y as a tensor of zeros with the same shape as x.Return a boolean tensor that yields Trues if x equals y element-wise.
Hint: Look up tf.equal().
import tensorflow as tf x=tf.constant([[0, -2, -1], [0, 1, 2]]) y=tf.zeros([2,3],tf.int32) r=tf.equal(x,y) sess=tf.Session() print(sess.run(r)) >>>>[[ True False False] [ True False False]]1d: Create the tensor x of value
[29.05088806, 27.61298943, 31.19073486, 29.35532951, 30.97266006, 26.67541885, 38.08450317, 20.74983215, 34.94445419, 34.45999146, 29.06485367, 36.01657104, 27.88236427, 20.56035233, 30.20379066, 29.51215172, 33.71149445, 28.59134293, 36.05556488, 28.66994858]. Get the indices of elements in x whose values are greater than 30. Hint: Use tf.where(). Then extract elements whose values are greater than 30.Hint: Use tf.gather().
import tensorflow as tf x=tf.constant([29.05088806, 27.61298943, 31.19073486, 29.35532951, 30.97266006, 26.67541885, 38.08450317, 20.74983215, 34.94445419, 34.45999146, 29.06485367, 36.01657104, 27.88236427, 20.56035233, 30.20379066, 29.51215172, 33.71149445, 28.59134293, 36.05556488, 28.66994858]) y=tf.where(tf.greater(x,30)) sess=tf.Session() print(sess.run(y)) >>>> [[ 2] [ 4] [ 6] [ 8] [ 9] [11] [14] [16] [18]] z=tf.gather(x,y) print(sess.run(z)) >>>> [[31.190735] [30.97266 ] [38.084503] [34.944454] [34.45999 ] [36.01657 ] [30.20379 ] [33.711494] [36.055565]]1e: Create a diagnoal 2-d tensor of size 6 x 6 with the diagonal values of 1,
2, ..., 6Hint: Use tf.range() and tf.diag().
import tensorflow as tf x=tf.range(1,7,1) y=tf.diag(x) sess=tf.Session() print(sess.run(y)) >>>>[[1 0 0 0 0 0] [0 2 0 0 0 0] [0 0 3 0 0 0] [0 0 0 4 0 0] [0 0 0 0 5 0] [0 0 0 0 0 6]]1f: Create a random 2-d tensor of size 10 x 10 from any distribution.
Calculate its determinant.Hint: Look at tf.matrix_determinant().
import tensorflow as tf x=tf.range(1.0,11.0,1.0) y=tf.diag(x) z=tf.matrix_determinant(y) sess=tf.Session() print(sess.run(y)) >>>> [[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 2. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 3. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 4. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 5. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 6. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 7. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 8. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 9. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 10.]] print(sess.run(z)) >>>>3628798.21g: Create tensor x with value [5, 2, 3, 5, 10, 6, 2, 3, 4, 2, 1, 1, 0, 9].
Return the unique elements in x Hint: use tf.unique(). Keep in mind that tf.unique() returns a tuple. import tensorflow as tf x=tf.constant([5, 2, 3, 5, 10, 6, 2, 3, 4, 2, 1, 1, 0, 9]) y, idx = tf.unique(x) sess=tf.Session() print(sess.run(y)) >>>>[ 5 2 3 10 6 4 1 0 9]1h: Create two tensors x and y of shape 300 from any normal distribution,
as long as they are from the same distribution. Use tf.cond() to return: - The mean squared error of (x - y) if the average of all elements in (x - y) is negative, or - The sum of absolute value of all elements in the tensor (x - y) otherwise.Hint: see the Huber loss function in the lecture slides 3.
import tensorflow as tf x=tf.random_normal([1,300],mean=0.0,stddev=1.0,dtype=tf.float32) y=tf.random_normal([1,300],mean=0.0,stddev=1.0,dtype=tf.float32) z=tf.cond(tf.reduce_mean(x-y)<0,lambda:tf.reduce_mean(tf.square(x-y)),lambda:tf.reduce_sum(tf.abs(x-y))) sess=tf.Session() print(sess.run(x-y)) >>>>[[-3.3259373 0.9305101 2.3369002 -0.58522654 -0.16759875 1.698406 -0.01081187 -0.38285708 1.111228 -2.0451121 0.72925764 -0.8763517 1.3866515 -0.7273851 -0.06797087 0.23708224 -0.4933051 0.55349827 0.2565738 1.3393756 -0.20295298 -0.37385103 1.4424682 -2.5382326 -1.4828734 1.7097936 2.525352 0.6961763 -1.6808228 -1.0919024 -1.7160004 2.0651374 0.08216521 -4.153361 1.1308309 -0.03906488 1.7629608 0.9418278 0.95242786 0.37612128 1.7995732 -1.8174422 0.68410325 1.1663442 -0.04805964 0.36871102 1.7314537 1.1640325 0.63783544 -1.4898503 -1.462168 -0.90315914 2.2180512 1.9490724 0.26858312 -0.7575321 -2.5997772 2.7222347 -0.27430427 0.14750266 -2.0072467 -0.8164351 0.3545426 1.8677943 -2.6482778 0.40069485 -0.27549598 -1.9823563 -0.7630851 -2.2795818 -2.0075269 0.48266673 0.6017053 2.1382527 0.74592113 0.52842444 0.32057723 0.26354694 -2.5238962 2.6234004 4.7944026 -1.6891562 1.2090372 -0.69063425 1.0070536 -0.9678581 0.5506362 -1.9133409 0.3949564 -0.24645442 0.37920615 -1.1195459 2.2429037 0.14914593 -1.7539029 2.558775 -0.6383449 -2.3427832 -0.09224594 -2.1342983 0.6265718 0.12264508 -1.0492 -0.7277551 1.5103204 -1.8051116 0.01116648 -1.8283169 0.8466557 -2.7166715 -1.6153333 -4.0796146 -1.8603649 -2.7653427 -1.2411971 -1.3313416 0.23849896 -1.4215461 -0.5723803 0.73624223 1.1147115 0.65772235 -2.1826386 1.0381981 2.572258 1.795631 0.37775195 0.8735537 0.14153415 -0.40607256 0.08104062 2.124076 -1.0336151 1.1516895 -0.9265008 -0.961225 -1.0439814 -3.0700302 1.8282708 2.992245 -1.7811463 2.088091 -2.154027 2.5574968 -1.7110771 0.15141404 2.189666 -0.8567083 -2.1762702 -0.42888075 3.3124957 0.946964 -1.6782995 1.3670723 0.16284399 1.9230621 -2.4625988 1.2417272 0.70477813 -0.9294472 0.39884406 -1.0117009 -0.84982616 -0.02290625 -2.5489736 -2.3589077 -0.7883153 0.05992937 -0.10870516 1.0378543 0.02198076 -0.46578634 -1.2035284 -2.4899156 -1.5938048 3.0138226 1.447144 -1.2254368 -0.07803547 -1.7945874 2.4832418 1.9498408 3.1883407 -0.25840232 -1.0816634 1.2577249 -0.43239972 0.9880579 -0.2810726 1.7623899 1.4618556 2.002539 0.34034097 -0.08139475 -0.70431256 1.1688669 0.6220495 0.05951071 2.2498481 -2.597868 1.3775531 0.80213875 -0.6430395 2.1536717 -0.21881579 -0.16325207 2.4478955 1.5137436 -0.2114343 -0.9714613 1.6356633 -0.2199794 -2.7332892 0.75113165 -3.7493258 -1.754692 -0.3192181 -0.2781701 1.024087 -4.010352 1.9129378 1.718173 1.3617702 -1.0772389 -0.40157917 -0.01206875 -1.4310075 0.38276523 -0.7008718 -1.0611877 -1.1379039 3.802422 -2.7665658 -1.0831838 -0.03419453 0.54294604 0.27439943 -0.07699788 0.6292731 2.9984536 0.87841225 -0.4740056 -0.04971766 -1.8112401 -0.01951165 0.9263905 0.22594452 0.3790323 -0.41561016 1.1175281 -1.425831 0.3882755 0.6556697 -0.61765623 -1.5916424 -0.6630892 -1.7987186 0.7655849 -0.5913538 -0.73421514 0.3323573 0.3332346 4.45142 -4.320508 1.4509175 -2.7783024 2.027264 0.4835115 -2.8464792 -0.31837556 -0.05154544 1.2853687 1.30814 -1.3599446 -0.10480693 0.03862008 -1.6713247 0.5779135 -1.0575099 -2.2695613 0.815313 -1.2145096 -0.60316 2.198226 -0.9850508 -0.7066151 -2.173722 1.5004234 -1.622364 -0.99073625 1.4605755 -0.18946862 2.4435315 0.3853131 1.6368191 0.22564127 -0.9333099 2.6863937 -2.6122236 -0.06404543]] print(sess.run(z)) >>>>311.44037