def distributed_dot_softmax(M, H):
"""
Obtains a matrix m of recent embeddings
and the hidden state h of the lstm
and calculates the attention distribution over embeddings
p = softmax(<m, h>)
M: embeddings tensor of shape
(batch_size, timesteps, history_length, units) or
(timesteps, history_length, units)
H: hidden state tensor of shape
(batch_size, timesteps, units) or
(timesteps, units)
:return:
"""
# flattening all dimensions of M except the last two ones
M_shape = kb.print_tensor(kb.shape(M))
M_shape = tuple((M_shape[i] for i in range(kb.ndim(M))))
new_M_shape = (-1, ) + M_shape[-2:]
H_shape = kb.print_tensor(kb.shape(H))
new_H_shape = (-1, H_shape[-1])
M_ = kb.reshape(M, new_M_shape)
# new_H_shape = kb.concatenate([np.array([-1]), kb.shape(H)[-2:]], axis=0)
H_ = kb.reshape(H, new_H_shape)
energies = kb.batch_dot(M_, H_, axes=[2, 1])
# Tensor representing shape is not iterable with tensorflow backend
answer = kb.reshape(kb.softmax(energies), M_shape[:-1])
if not hasattr(answer, "_keras_shape") and hasattr(M, "_keras_shape"):
answer._keras_shape = M._keras_shape[:-1]
return answer
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
K.print_tensor(intersection, message="Dice intersection:")
return -((2. * intersection + K.epsilon()) /
(K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon()))
def self_aware_loss(y_true, y_pred):
abstain_penalty = 2. # ea
adversarial_penalty = 10. # eq
ea = tf.constant(abstain_penalty, shape=(1, ))
eq = tf.constant(adversarial_penalty, shape=(1, ))
label_true = tf.argmax(y_true)
label_pred = tf.argmax(y_pred)
xe = tf.nn.softmax(logits=y_pred)
# we want to compute a whole lottsa shit
p1 = y_pred[1]
p11 = K.constant(1) - K.gather(y_pred, label_true[0])
p12 = tf.multiply(p1, p11)
p2 = tf.multiply(y_pred[0], eq)
ai = p12 + p2
predict_options = K.concatenate([eq, p11])
def f1():
return K.gather(predict_options, label_pred)
def f2():
return ea
L = tf.cond(tf.less(ai[0], ea[0]), f1, f2)
K.print_tensor(ai)
K.print_tensor(ea)
return p12
return L
def binary_crossentropy_MConly_Delphes(y_true, y_pred):
"""
"""
printAll=False
if printAll:
y_pred=K.print_tensor(y_pred,' labelpred ')
y_true=K.print_tensor(y_true,' ytrueLabel ')
# the prediction if it is data or MC is in the first index (see model)
labelpred = y_pred
# the truth if it is data or MC, 0 for data
isMCtrue = y_true[:,:1]
# labels: B, C, UDSG
labels_true = y_true[:,1:]
if printAll:
isMCtrue=K.print_tensor(isMCtrue,' MCtruth')
labels_true=K.print_tensor(labels_true,' labels')
weighted_xentr = isMCtrue*K.binary_crossentropy(labelpred, labels_true)
if printAll:
weighted_xentr= K.print_tensor(weighted_xentr,' weighted xent ')
out=K.mean( weighted_xentr )
#sum weight again over all samples
return out
def train(dataset, epochs):
for epoch in range(epochs):
start = time.time()
for image_batch in dataset:
x, p_labels = image_batch
(gen_loss, disc_loss) = train_step(x, p_labels)
K.print_tensor(gen_loss, message='gen loss =')
K.print_tensor(disc_loss, message='disc loss =')
# generate and save at each epoch
plot_predictions(generator, seed_start, seed_end, seed)
# plot_predictions(generator, seed)
generator.save(model_file)
# Save the model every 5 epochs
if (epoch + 1) % 5 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
print('Time for epoch {} is {} sec'.format(epoch + 1,
time.time() - start))
def call(self, x):
shape = K.int_shape(x)
# print(shape)
# the_probs = K.mean(x)
# the_probs = K.print_tensor(the_probs, message="the probs now are: ")
# the_probs = np.repeat(the_probs, x_len)
# backup = x
dolog = False
if dolog:
x = K.print_tensor(x, message="[0] the x now is: ")
x = K.mean(x, axis=[0], keepdims=True)
if dolog:
x = K.print_tensor(x, message="[1] the x now is: ")
x = K.repeat_elements(x, 1, axis=0)
if dolog:
x = K.print_tensor(x, message="[2] the x now is: ")
x = K.reshape(x, shape=(-1, self.output_dim))
# x = K.permute_dimensions(x, (1, self.output_dim))
if dolog:
x = K.print_tensor(x, message="[3] the x now is: ")
# raise Exception
# x = backup
return x
def dual_loss(y_true, y_pred):
# This function creates a graph that computes the Triplet Loss, the Regression MSE and fuses both.
# To be compatible with keras, the only parameters this function can have are <y_true> and <y_pred>.
if verbose: y_true = K.print_tensor(y_true, message="y_true : ")
if verbose: y_pred = K.print_tensor(y_pred, message="y_pred : ")
tripletLoss = tripletLoss_Wrapper(
fvSize=fvSize, margin=margin, verbose=verbose)(
y_true,
y_pred) # Creating the subgraph that computes the Triplet Loss
if verbose:
tripletLoss = K.print_tensor(tripletLoss, message="tripletLoss : ")
y_pred_cropped = y_pred[:, 3 * fvSize:]
y_true_cropped = y_true[:, 3 * fvSize:]
# regressionLoss = regressionMSE_Wrapper(fvSize=fvSize, verbose=verbose)(y_true_cropped, y_pred_cropped) # Creating the subgraph that computes the regression Loss
regressionLoss = regressionCE_Wrapper(fvSize=fvSize)(
y_true_cropped, y_pred_cropped
) # Creating the subgraph that computes the regression Loss
if verbose:
regressionLoss = K.print_tensor(regressionLoss,
message="regressionLoss : ")
# dualLoss = tripletLoss * regressionLoss # Fusing both losses
dualLoss = tripletLoss * tripletToRegressionRatio + regressionLoss * (
1 - tripletToRegressionRatio) # Fusing both losses
if verbose: dualLoss = K.print_tensor(dualLoss, message="dualLoss : ")
return dualLoss # The tensor named "dualLoss"
def huber_loss_print(x, y):
x = b.print_tensor(x, message="x= ")
y = b.print_tensor(y, message="y= ")
e = x - y
e = b.print_tensor(e, message="e= ")
loss = b.mean(b.sqrt(1 + b.square(e)) - 1, axis=-1)
loss = b.print_tensor(loss, message="loss= ")
return loss
def test_geom_type_loss(self):
tensor1 = np.array(
[[[1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], dtype=float)
tensor2 = np.array(
[[[1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], dtype=float)
K.print_tensor(tensor1)
loss = geom_gaussian_loss(tensor1, tensor2).eval()
self.assertEqual(loss, 3.26972228)
def loss(y_data, y_pred):
y_true = [y_data[:, i] for i in range(4)]
y_pred = [y_pred[:, i] for i in range(4)]
sample_weights = y_data[:, 4]
class_weights = y_data[:, 5]
def crossentropy(i):
true = y_true[i]
pred = y_pred[i]
score = keras.losses.categorical_crossentropy(y_true[i], y_pred[i])
return weight(score, class_weights)
def significance(i):
s_w = expsig / K.sum(y_true[i])
b_w = expbg / K.sum(1 - y_true[i])
s = s_w * K.sum(sample_weights * y_pred[i] * y_true[i])
b = b_w * K.sum(sample_weights * y_pred[i] * (1 - y_true[i]))
b = tf.cond(b < 0, lambda: tf.constant(0.), lambda: b)
return (s + b) / (s * s + K.epsilon())
def asimov(i):
s_w = expsig / K.sum(sample_weights * y_true[i])
b_w = expbg / K.sum(sample_weights * (1 - y_true[i]))
s = s_w * K.sum(sample_weights * y_pred[i] * y_true[i])
b = b_w * K.sum(sample_weights * y_pred[i] * (1 - y_true[i]))
b = tf.maximum(tf.constant(2.), b)
sigB = sigma * b
ln1_top = (s + b) * (b + sigB * sigB)
ln1_bot = b * b + (s + b) * sigB * sigB
ln1 = K.log(ln1_top / (ln1_bot + K.epsilon()) + K.epsilon())
ln2 = K.log(1. + sigB * sigB * s /
(b * (b + sigB * sigB) + K.epsilon()))
loss = 1. / K.sqrt(2 * ((s + b) * ln1 - b * b * ln2 /
(sigB * sigB + K.epsilon())) + K.epsilon())
# loss = weight(loss, sample_weights)
return loss
cc = crossentropy(0) \
+ crossentropy(1) \
+ crossentropy(2) \
+ crossentropy(3)
asi = asimov(3)
if debug:
cc = K.print_tensor(cc, "cc =")
asi = K.print_tensor(asi, "asi =")
if name == 'crossentropy':
return cc
elif name == 'asimov':
return asi + cc * cc_weight
else:
print("\x1b[38;5;1;1mError:\x1b[0m undefined loss name, {}".format(
name))
raise Exception("undefined loss")
def n0_dft(n0_scaled):
n0_scaled = K.print_tensor(n0_scaled, "n0_scaled is: ")
n0 = n0_scaled * gain #*P_max
n0 = K.print_tensor(n0, "n0 is: ")
#note n0_scaled = n0/P_max such that n0_scaled stays betwen [0..1]
N = width
cos_term = K.cos(n0 * K.cast(K.arange(N), dtype='float32') * np.pi / N)
sin_term = K.sin(-n0 * K.cast(K.arange(N), dtype='float32') * np.pi / N)
return K.concatenate([cos_term, sin_term], axis=-1)
def mean_pred(y_true, y_pred):
print("----------------")
y_true = K.print_tensor(y_true, message='y_true = ')
y_pred = K.print_tensor(y_pred, message='y_pred = ')
y_pred_idx = K.argmax(y_pred, axis=-1)
y_pred_idx = K.cast( y_pred_idx, K.dtype(y_true) )
print('-------------',K.dtype(y_pred_idx))
compare = K.equal(y_true, y_pred_idx)
compare = K.print_tensor(compare, message='compare = ')
return compare
def _iou_metric(y_true, y_pred, epsilon=1e-5, sequence_length=5):
""" Inspired by: http://ronny.rest/tutorials/module/localization_001/intersect_of_union/
Given two arrays `y_true` and `y_pred` where each row contains a bounding
boxes for sequence of digits. By default, sequence length is 5. Each digit is represented by 4 numbers:
[y1, x1, y2, x2]
where:
x1,y1 represent the upper left corner
x2,y2 represent the lower right corner
It returns the Intersect of Union scores for each sequence.
Args:
y_true: (numpy array) sequence_length * each row containing [y1, x1, y2, x2] coordinates
y_pred: (numpy array) sequence_length * each row containing [y1, x1, y2, x2] coordinates
epsilon: (float) Small value to prevent division by zero
sequence_length: (int) number of digits in the sequence
Returns:
(float) Sum of IoU for all digits in sequence
"""
# Reshape the sequence coordinates which comes flatten from the regressor.
y_true = K.reshape(y_true, [-1, 5, 4])
y_pred = K.reshape(y_pred, [-1, 5, 4])
K.print_tensor(y_true, "OLOLO")
# COORDINATES OF THE INTERSECTION BOXES
y1 = K.maximum(y_true[:, :, 0], y_pred[:, :, 0])
x1 = K.maximum(y_true[:, :, 1], y_pred[:, :, 1])
y2 = K.minimum(y_true[:, :, 2], y_pred[:, :, 2])
x2 = K.minimum(y_true[:, :, 3], y_pred[:, :, 3])
# AREAS OF OVERLAP - Area where the boxes intersect
width = (x2 - x1)
height = (y2 - y1)
# handle case where there is NO overlap
width = K.clip(width, 0, None)
height = K.clip(height, 0, None)
# area_overlap = width * height
# area_overlap = K.prod(width * height)
area_overlap = K.tf.multiply(width, height)
# COMBINED AREAS
area_a = (y_true[:, :, 2] - y_true[:, :, 0]) * (y_true[:, :, 3] - y_true[:, :, 1])
area_b = (y_pred[:, :, 2] - y_pred[:, :, 0]) * (y_pred[:, :, 3] - y_pred[:, :, 1])
area_combined = area_a + area_b - area_overlap
# RATIO OF AREA OF OVERLAP OVER COMBINED AREA
iou = area_overlap / (area_combined + epsilon)
iou = K.mean(iou) # reduce mean across all axis
return iou
def gaussian_kl_loss(z_mean, z_log_var, params, debug):
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
if debug:
z_mean = K.print_tensor(z_mean, "\nz_mean")
z_log_var = K.print_tensor(z_log_var, "\nz_log_var")
kl_loss = K.print_tensor(kl_loss, "\nkl_loss")
return kl_loss
def loss(y_true, y_pred):
with K.name_scope('regression_loss'):
if not K.is_tensor(y_pred):
y_pred = K.constant(y_pred)
y_true /= 100. # because our data is max 100 GeV
if DEBUG:
y_pred = K.print_tensor(y_pred, message='pred')
y_true = K.print_tensor(y_true, message='true')
return K.mean(K.square(y_true - y_pred) / y_true, axis=-1)
def critic_optimizer(): R = K.placeholder(shape=(None,)) critic = model_critic.output critic = K.print_tensor(critic, message='critic: ') Lv = K.mean(K.square(R - critic)) Lv = K.sum(Lv) Lv = K.print_tensor(Lv, message='Lv: ') optimizer = RMSprop(lr=2.5e-4, rho=0.99, epsilon=0.01) #optimizer = my_optimizer_critic updates = optimizer.get_updates(model_critic.trainable_weights, [], Lv) train = K.function([model_critic.input, R], [Lv], updates=updates) return train
def call(self, input):
query, values = input
# query = K.print_tensor(query, 'query')
# values = K.print_tensor(values, 'values')
# hidden shape == (batch_size, hidden size)
# hidden_with_time_axis shape == (batch_size, 1, hidden size)
# we are doing this to perform addition to calculate the score
hidden_with_time_axis = K.expand_dims(query, 1)
# score shape == (batch_size, max_length, hidden_size)
score = self.V(
K.tanh(self.W1(values) + self.W2(hidden_with_time_axis)))
# score = K.print_tensor(score, 'score')
# attention_weights shape == (batch_size, max_length, 1) # 32?
# we get 1 at the last axis because we are applying score to self.V
attention_weights = K.softmax(score, axis=1)
# context_vector shape after sum == (batch_size, hidden_size)
context_vector = attention_weights * values
context_vector = K.sum(context_vector, axis=1)
attention_weights = K.print_tensor(attention_weights,
'attention_weights')
return [context_vector, attention_weights]
def CCC4Keras(y_pred, y_true):
K.print_tensor(y_true, message='y_true = ')
pc = PearsonCorrelation4keras(y_true, y_pred)
devP = K.std(y_pred, axis=0)
devT = K.std(y_true, axis=0)
meanP = K.mean(y_pred, axis=0)
meanT = K.mean(y_true, axis=0)
powMeans = K.pow(meanP-meanT,2)
varP = K.var(y_pred, axis=0)
varT = K.var(y_true, axis=0)
numerator = 2*pc*devP*devT
denominator = varP+varT+powMeans
CCC = numerator/denominator
return K.sum(CCC)
def sampling(args, operation):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim), mean=0.0, stddev=1.0)
if debug:
z_mean = K.print_tensor(z_mean,
"sampling" + operation + " z_mean")
z_log_var = K.print_tensor(
z_log_var, "sampling" + operation + " z_log_var")
epsilon = K.print_tensor(epsilon,
"sampling" + operation + " epsilon")
latent_space = z_mean + K.exp(0.5 * z_log_var) * epsilon
if debug:
latent_space = K.print_tensor(
latent_space, "sampling" + operation + " latent_space")
return latent_space
def gaussian_kl_divergence(m, v, params, debug):
ds = tf.contrib.distributions
p = ds.Normal(loc=m[0], scale=v[0])
q = ds.Normal(loc=m[1], scale=v[1])
kl = ds.kl_divergence(p, q)
if debug:
kl = K.print_tensor(kl, "\nkl_divergence")
return kl
def addNoise(x, sigma, len_test=None):
if len_test is None:
w = K.random_normal(K.shape(x), mean=0.0, stddev=sigma)
positives = K.equal(x, 3)
positives = K.cast(positives, K.floatx())
noisy = x + w
noisy = noisy - noisy * positives + 3 * positives
K.print_tensor(noisy)
return noisy
else:
w = np.random.normal(0.0, sigma, x.shape)
noisy = x + w
for noisy_test_i in range(0, noisy.shape[0]):
if len_test[noisy_test_i][0] < noisy.shape[1]:
noisy[noisy_test_i][int(len_test[noisy_test_i][0]):] = [3] * (
noisy.shape[1] - int(len_test[noisy_test_i][0]))
return noisy
def custom_loss(y_true, y_pred):
x = y_true
y = y_pred * macd
y = tf.Print(y, [y], 'refactored indicator', summarize=4000)
mx = K.mean(x)
mx = K.print_tensor(mx, message="mx = ")
my = K.mean(y)
my = K.print_tensor(my, message="my = ")
xm, ym = x - mx, y - my
r_num = K.sum(tf.multiply(xm, ym))
r_den = K.sqrt(tf.multiply(K.sum(K.square(xm)), K.sum(K.square(ym))))
r = r_num / r_den
r = K.maximum(K.minimum(r, 1.0), -1.0)
return 1 - K.square(r)
def binary_crossentropy(y_true, y_pred):
l2 = 0.001
y_true = K.print_tensor(y_true, 'y_true: ')
print_y_pred = K.tf.print({'y_pred': y_pred}, summarize=-1)
with K.tf.control_dependencies([print_op, print_y_pred]):
loss = K.mean(K.binary_crossentropy(y_true, y_pred),
axis=-1) + K.sum(l2 * K.square(layer))
loss = K.tf.Print(loss, [loss], 'loss is: ', summarize=-1)
return loss
def gaussian_reconstruction_loss(inputs, outputs, output_dim, params):
if params['reconstruction_loss'] == 'mse':
reconstruction_loss = mse(inputs, outputs)
elif params['reconstruction_loss'] == 'binary_crossentropy':
reconstruction_loss = binary_crossentropy(inputs, outputs)
if debug:
reconstruction_loss = K.print_tensor(
reconstruction_loss, "\ngaussian reconstruction_loss")
reconstruction_loss *= output_dim
if debug:
reconstruction_loss = K.print_tensor(
reconstruction_loss,
"\ngaussian reconstruction_loss scaled up")
return reconstruction_loss
def D_Loss(y_true, y_pred):
# Sample from the gaussian distribution.
D_x = y_pred[:512]
D_y = y_pred[512:]
#Calculate and print the loss
loss = -(K.mean(K.log(1 - D_y)) + K.mean(K.log(D_x)))
loss = K.print_tensor(loss)
return loss
def asimovSigLossInvert(y_true, y_pred):
signalWeight = expectedSignal / K.sum(y_true)
bkgdWeight = expectedBkgd / K.sum(1 - y_true)
# signalWeight = 1.#expectedSignal/K.sum(y_true)
# bkgdWeight = 1.#expectedBkgd/K.sum(1-y_true)
s = signalWeight * K.sum(y_pred * y_true)
b = bkgdWeight * K.sum(y_pred * (1 - y_true))
b = tf.cond(b < 2, lambda: tf.constant(2.), lambda: b)
# b = K.print_tensor(b, "b =")
sigB = systematic * b
ln1_top = (s + b) * (b + sigB * sigB)
ln1_bot = b * b + (s + b) * sigB * sigB
ln1 = K.log(ln1_top / (ln1_bot + K.epsilon()) + K.epsilon())
ln2 = K.log(1. + sigB * sigB * s / (b *
(b + sigB * sigB) + K.epsilon()))
if debug:
s = K.print_tensor(s, message='s = ')
b = K.print_tensor(b, message='b = ')
sigB = K.print_tensor(sigB, message='sigB = ')
ln1 = K.print_tensor(ln1, message='ln1 = ')
ln2 = K.print_tensor(ln2, message='ln2 = ')
loss = 1. / (2 * ((s + b) * ln1 - b * b * ln2 /
(sigB * sigB + K.epsilon())) + K.epsilon()
) #Add the epsilon to avoid dividing by 0
if debug:
loss = K.print_tensor(loss, message='loss = ')
return loss
def average_fn(x):
# print(x.get_shape()[1])
shape = x.get_shape()
# try:
# print(K.get_value(K.mean(x)))
# except: # Exception(x):
# print("get_value raised an exc")
# #print(x)
# try:
# print(K.batch_get_value(x))
# except: #Exception(x):
# print("batch_get_value raised an exc")
# #print(x)
# raise Exception
# x_length = len(x)
# the_probs = sum(x) / x_length
# the_probs = the_probs.reshape(1, -1)
# the_probs = np.repeat(the_probs, x_length, axis=0)
# return the_probs
the_probs = K.mean(x)
the_probs = K.print_tensor(the_probs, message="the probs now are: ")
# the_probs = np.repeat(the_probs, x_len)
backup = x
x = K.print_tensor(x, message="the x now is: ")
x = K.mean(x, axis=[1])
# x = K.repeat_elements(x, shape[1], axis=0)
x = K.reshape(x, shape=(self.input_shape, shape[1]))
x = K.print_tensor(x, message="the x now is: ")
x = backup
return x
def asimovSigLossInvert(y_true, y_pred):
l = losses(y_true, y_pred)
crossentropy = l['crossentropy']
asimov = l['asimov']
sample_weights = l['sample_weights']
class_weights = l['class_weights']
def weight(score, weights):
score_arr = (score * weights) \
/ K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
return K.mean(score_arr)
loss_bg = weight(crossentropy(0), class_weights) \
+ weight(crossentropy(1), class_weights) \
+ weight(crossentropy(2), class_weights) \
+ weight(crossentropy(3), class_weights)
loss_sig = asimov(3)
if debug:
loss_bg = K.print_tensor(loss_bg, "loss_bg = ")
loss_sig = K.print_tensor(loss_sig, "loss_sig = ")
return loss_sig + 1e-2 * loss_bg
def paper_regulariser(weights):
'''Purpose is to make topic vectors perpendicular, i.e. their
dot product should be zero'''
normalised_weights = weights / tf.sqrt(
tf.reduce_sum(tf.square(weights), axis=0, keepdims=True))
dot_prod_between_topic_matrices = tf.matmul(
tf.transpose(normalised_weights), normalised_weights)
dot_prod_between_topic_matrices = K.print_tensor(
dot_prod_between_topic_matrices, "dot_prod_between_topic_matrices = ")
minus_identity_matrix = dot_prod_between_topic_matrices - tf.eye(11)
absolute_value = tf.abs(minus_identity_matrix)
sum_columns = reduce_sum(absolute_value, axis=0)
sum_all_elements = reduce_sum(sum_columns)
return sum_all_elements
def return_heatmap(self, model, org_img,normalise = True):
"""CAM implementation here. An activation heatmap is produced for every test images. """
test_img = model.output[:, 1]
if self.model_type == 'simple':
last_conv_layer = model.get_layer('conv2d_3')
else:
last_conv_layer = model.get_layer('conv2d_6')
grads = K.gradients(test_img, last_conv_layer.output)[0]
pooled_grads = K.mean(grads, axis=(0, 1, 2))
message = K.print_tensor(pooled_grads, message='pool_grad = ')
iterate = K.function([model.input],
[message, last_conv_layer.output[0]])
pooled_grads_value, conv_layer_output_value = iterate([org_img.reshape(-1, self.img_rows, self.img_cols, self.c_dim)])
heatmap = np.mean(conv_layer_output_value, axis=-1)
if normalise:
heatmap = np.maximum(heatmap, 0)
heatmap /= np.max(heatmap)
return heatmap
from __future__ import print_function
import math
import keras
import numpy
from keras import backend as K
print("math.exp(4):", math.exp(4))
print(numpy.random.rand(3,2))
#K_square = K.square(numpy.random.rand(3,2))
K_square = K.print_tensor(K.square, message = "keras.square() is ")
K_square = K.square(numpy.random.rand(3,2))
#print("keras.square():", keras.square(numpy.random.rand(3,2)))