coeffs = 4 * np.random.random(n) - 2
splineSpace = len(coeffs)
coeffs = np.array([coeffs])
knots = np.zeros(n + p + 1)
knots[:p + 1] = 0
knots[-p - 1:] = n - p
knots[p + 1:-p - 1] = np.sort(
np.random.random(len(knots[p + 1:-p - 1])) *
(knots[-1] - knots[0])) + knots[0]
sampleStep = int((epochs - 1) / (samples - 1))
x = tf.placeholder(tf.float32, [None, 1])
z = tf.placeholder(tf.float32, [None, 1])
tz, c = splineParameters(nodes, splineSpace, p, [knots[0], knots[-1]])
t = sortKnot(tz, p)
y = spline(x, t, c, p)
loss = tf.reduce_mean((y - z)**2)
optimizer = tf.train.AdamOptimizer(2e-3).minimize(loss)
config = tf.ConfigProto()
config.intra_op_parallelism_threads = 12
config.inter_op_parallelism_threads = 12
tees = np.empty([samples, len(knots)])
cees = np.empty([samples, len(coeffs.T)])
tzees = np.empty([samples, len(knots)])
errors = np.empty(samples)
except: p = 3 samples = 15 for sample in range(samples): #Neural network architecture x = tf.placeholder(tf.float32, [None, inputSize]) t = tf.placeholder(tf.float32, [None, 1]) W1 = randomVariable([inputSize, hiddenSize]) b1 = randomVariable([hiddenSize]) W2 = randomVariable([hiddenSize, outputSize]) b2 = randomVariable([outputSize]) t1, c1 = splineParameters(hiddenSize, splineSize, p, [-2, 2]) t2, c2 = splineParameters(outputSize, splineSize, p, [-2, 2]) h = spline(tf.matmul(x, W1) + b1, t1, c1, p) y = spline(tf.matmul(h, W2) + b2, t2, c2, p) # y = tf.matmul(h, W2) + b2 cost = tf.sqrt(tf.losses.mean_squared_error(t, y)) _, var = tf.nn.moments(t, axes = [0]) nrmse = tf.sqrt(tf.losses.mean_squared_error(t, y)/var[0]) optimizer = tf.train.AdamOptimizer(1e-3).minimize(cost) minError = 1e6 with tf.Session() as sess: sess.run(tf.global_variables_initializer())
tf.random_uniform(shape=tf.shape(x),
minval=0,
maxval=2,
dtype=tf.int32), tf.float32))
learningRateAE = 1e-3
learningRateFT = 1e-4
lossFunction = tf.losses.mean_squared_error
displayImages = mnist.validation.images[[3, 5, 7, 9, 0, 66, 71, 85, 4]]
x = tf.placeholder(tf.float32, [None, inputSize])
target = tf.placeholder(tf.float32, [None, outSize])
tz1, C1 = splineParameters(hidden1,
splineSpaceSize,
p, [-5, 5],
name="spline1")
tz2, C2 = splineParameters(hidden2,
splineSpaceSize,
p, [-5, 5],
name="spline2")
tz3, C3 = splineParameters(outSize,
splineSpaceSize,
p, [-5, 5],
name="spline3")
_, C1T = splineParameters(inputSize,
splineSpaceSize,
p, [-5, 5],
name="spline1T")
_, C2T = splineParameters(hidden1,
splineSpaceSize,