I am having a problem calculating the gradient in TensorFlow 1.15. I think it's something related context manager or keras session, but I am not sure about it. Following is the code I have written:
def create_adversarial_pattern_CW(input_patch, input_label, target_label):
input_patch_T = tf.cast(input_patch,tf.float32)
with tf.GradientTape() as tape:
tape.watch(input_patch_T)
patch_pred = model(input_patch_T)
loss_input_label = soft_dice_loss(input_label, patch_pred[0])
loss_target_label = soft_dice_loss(target_label, patch_pred[0])
f = loss_input_label - loss_target_label
f_grad = tape.gradient(f, input_patch_T)
#-------------------------#
print(type(f_grad))
#-------------------------#
f_grad_sign = tf.sign(f_grad)
return f_grad_sign
def DAG():
sess = K.get_session()
with sess.as_default() as sess:
adv_x_old = tf.cast(X,dtype=tf.float32)
for i in range(iters):
#-------------------------#
#y_pred = model(adv_x_old) -> If I uncomment this line the value of f_grad returned is None, otherwise it works fine, but I need this line
#-------------------------#
perturbations = create_adversarial_pattern_CW(adv_x_old, y, y_target)
adv_x_new = adv_x_old - alpha*perturbations
adv_x_old = adv_x_new
adv_patch_pred = model(adv_x_old)
To fix it, I tried to wrap the commented line as :
with tf.GradientTape() as tape:
with tape.stop_recording():
y_pred = model(adv_x_old)
but I still get the value of f_grad as None.
Related
I have FailedPreconditionError when running sess.
My network has two different parts, pretrained-network and new add in Recognition network.
Pretrained model is used to extract features and the feature is used to train again for recognition.
In my code, pre-trained model is loaded first.
graph = tf.Graph()
with graph.as_default():
input_data, input_labels, input_boxes = input_train_data.input_fn()
input_boxes = tf.reshape(input_boxes,[input_boxes.shape[0]*2,-1])#convert from Nx8 to 2Nx4
# build model and loss
net = Net(input_data, is_training = False)
f_saver = tf.train.Saver(max_to_keep=1000, write_version=tf.train.SaverDef.V2, save_relative_paths=True)
sess_config = tf.ConfigProto(log_device_placement = False, allow_soft_placement = True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction;
session = tf.Session(graph=graph, config=sess_config)
tf.logging.info('Initialize from: ' + config.train.init_checkpoint)
f_saver.restore(session, config.train.init_checkpoint)
f_saver restores the pre-trained model.
Then feature conv5_3 is extracted and fed into Recognition network.
conv5_3 = net.end_points['conv5_3']
with tf.variable_scope("Recognition"):
global_step_rec = tf.Variable(0, name='global_step_rec', trainable=False)
#Pass through recognition net
r_net = regnet.ConstructRecNet(conv5_3)
conv7_7 = r_net.end_points['pool7']
#implement ROI Pooling
#input boxes be in x1, y1, x2, y2
h_fmap = tf.dtypes.cast(tf.shape(conv7_7)[1],tf.float32)
w_fmap = tf.dtypes.cast(tf.shape(conv7_7)[2],tf.float32)
#remap boxes at input images to feature mats
#input_boxes = input_boxes / tf.constant([config.train.input_shape[0], config.train.input_shape[0],\
# config.train.input_shape[0], config.train.input_shape[0]], dtype=tf.float32)#Normalize with image size first
remap_boxes=tf.matmul(input_boxes,tf.diag([w_fmap,h_fmap,w_fmap,h_fmap]))
#put first column with image indexes
rows = tf.expand_dims(tf.range(remap_boxes.shape[0]), 1)/2
add_index = tf.concat([tf.cast(rows,tf.float32),remap_boxes],-1)
index = tf.not_equal(tf.reduce_sum(add_index[:,4:],axis=1),0)
remap_boxes = tf.gather_nd(add_index,tf.where(index))
remap_boxes=tf.dtypes.cast(remap_boxes,tf.int32)
prob = roi_pooling(conv7_7, remap_boxes, pool_height=1, pool_width=28)
#Get features for CTC training
prob = tf.transpose(prob, (1, 0, 2)) # prepare for CTC
data_length = tf.fill([tf.shape(prob)[1]], tf.shape(prob)[0]) # input seq length, batch size
ctc = tf.py_func(CTCUtils.compute_ctc_from_labels, [input_labels], [tf.int64, tf.int64, tf.int64])
ctc_labels = tf.to_int32(tf.SparseTensor(ctc[0], ctc[1], ctc[2]))
predictions = tf.to_int32(tf.nn.ctc_beam_search_decoder(prob, data_length, merge_repeated=False, beam_width=10)[0][0])
tf.sparse_tensor_to_dense(predictions, default_value=-1, name='d_predictions')
tf.reduce_mean(tf.edit_distance(predictions, ctc_labels, normalize=False), name='error_rate')
loss = tf.reduce_mean(tf.compat.v1.nn.ctc_loss(inputs=prob, labels=ctc_labels, sequence_length=data_length, ctc_merge_repeated=True), name='loss')
learning_rate = tf.train.piecewise_constant(global_step_rec, [150000, 200000],[config.train.learning_rate, 0.1 * config.train.learning_rate,0.01 * config.train.learning_rate])
opt_loss = tf.contrib.layers.optimize_loss(loss, global_step_rec, learning_rate, config.train.opt_type,config.train.grad_noise_scale, name='train_step')
tf.global_variables_initializer()
I can run sess till feature extraction conv5_3. But can't run those in Recognition and got error as FailedPreconditionError: FailedPr...onError(). What could be the problem?
graph.finalize()
with tf.variable_scope("Recognition"):
for i in range(config.train.steps):
input_data_, input_labels_, input_boxes_ = session.run([input_data, input_labels, input_boxes])
conv5_3_ = session.run([conv5_3]) #can run this line
global_step_rec_ = session.run([global_step_rec]) # got FailedPreconditionError: FailedPr...onError() error at this line
conv7_7_ = session.run([conv7_7])
h_fmap_ = session.run([h_fmap])
Now it works.
Since my graph has two parts, I need to initialize separately.
(1)First get all variables from pre-trained model to initialize with those from checkpoint.
Then initialize with tf.train.Saver.
(2)Then initialize the rest add-in layers using tf.global_variables_initializer()
My code is as follow.
#Initialization
#Initialize pre-trained model first
#Since we need to restore pre-trained model and initialize to respective variables in this current graph
#(1)make a variable list for checkpoint
#(2)initialize a saver for the variable list
#(3)then restore
#(1)
def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors):
varlist=[]
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
if all_tensors:
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
print(key)
varlist.append(key)
return varlist
varlist=print_tensors_in_checkpoint_file(file_name=config.train.init_checkpoint,all_tensors=True,tensor_name=None)
#(2)prepare the list of variables by calling variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
#countcheckpt_vars=0
#for n in tf.get_default_graph().as_graph_def().node:
# print(n.name)
#for op in tf.get_default_graph().get_operations():
# print(str(op.name))
#for var in zip(variables):
# countcheckpt_vars=countcheckpt_vars+1
#(3)
loader = tf.train.Saver(variables[:46])#since I need to initialize only 46 variables from global variables
tf.logging.info('Initialize from: ' + config.train.init_checkpoint)
sess_config = tf.ConfigProto(log_device_placement = False, allow_soft_placement = True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction;
session = tf.Session(graph=graph, config=sess_config)
loader.restore(session, config.train.init_checkpoint)
Then initialize the rest of variables
init = tf.global_variables_initializer()
session.run(init)
I am dealing with an issue in my conversion over to the Dataset API and I guess I just don't have enough experience yet with the API to know how to handle the below situation. We currently have image augmentation that we perform currently using queueing and batching. I was tasked with checking out the new Dataset API and converting over our existing implementation using it rather than queues.
What we would like to do is get a reference to all the paths and handle all operations from just that reference. As you see in the dataset initialization, I have mapped the parse_fn to the dataset itself which then goes about reading the file and extracting the initial values from the filenames. However when I then go about calling the iterators next_batch method and then pass those values to get_summary, I'm now getting an error around shape. I have been trying a number of things which just keeps changing the error and so I felt I should see if anyone on SO saw possibly that I was going about this all wrong and should be taking a different route. Does anything jump out as absolutely wrong in my use of the Dataset API?
Should I not be calling the ops this way any longer? I noticed the majority of the examples I saw they would get the batch, pass the variables to the op and then capture that in a variable and pass that to sess.run, however I haven't found an easy way of doing that as of yet with our setup that wasn't erroring so this was the approach I took instead (but its still erroring). I'll be continuing to try to trace down the problem and post here should I find anything, but if anyone sees something please advise. Thanks!
Current Error:
... in get_summary summary, acc = sess.run([self._summary_op,
self._accuracy], feed_dict=feed_dict) ValueError: Cannot feed value of
shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
Below is the block where the get_summary method is called and error is fired:
def perform_train():
if __name__ == '__main__':
#Get all our image paths
filenames = data_layer_train.get_image_paths()
next_batch, iterator = preproc_image_fn(filenames=filenames)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
with sess.graph.as_default():
# Set the random seed for tensorflow
tf.set_random_seed(cfg.RNG_SEED)
classifier_network = c_common.create_model(len(products_to_class_dict), is_training=True)
optimizer, global_step_var = c_common.create_optimizer(classifier_network)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
# Init tables and dataset iterator
sess.run(tf.tables_initializer())
sess.run(iterator.initializer)
cur_epoch = 0
blobs = None
try:
epoch_size = data_layer_train.get_steps_per_epoch()
num_steps = num_epochs * epoch_size
for step in range(num_steps):
timer_summary.tic()
if blobs is None:
#Now populate from our training dataset
blobs = sess.run(next_batch)
# *************** Below is where it is erroring *****************
summary_train, acc = classifier_network.get_summary(sess, blobs["images"], blobs["labels"], blobs["weights"])
...
Believe the error is in preproc_image_fn:
def preproc_image_fn(filenames, images=None, labels=None, image_paths=None, cells=None, weights=None):
def _parse_fn(filename, label, weight):
augment_instance = False
paths=[]
selected_cells=[]
if vals.FIRST_ITER:
#Perform our check of the path to see if _data_augmentation is within it
#If so set augment_instance to true and replace the substring with an empty string
new_filename = tf.regex_replace(filename, "_data_augmentation", "")
contains = tf.equal(tf.size(tf.string_split([filename], "")), tf.size(tf.string_split([new_filename])))
filename = new_filename
if contains is True:
augment_instance = True
core_file = tf.string_split([filename], '\\').values[-1]
product_id = tf.string_split([core_file], ".").values[0]
label = search_tf_table_for_entry(product_id)
weight = data_layer_train.get_weights(product_id)
image_string = tf.read_file(filename)
img = tf.image.decode_image(image_string, channels=data_layer_train._channels)
img.set_shape([None, None, None])
img = tf.image.resize_images(img, [data_layer_train._target_height, data_layer_train._target_width])
#Previously I was returning the below, but I was getting an error from the op when assigning feed_dict stating that it didnt like the dictionary
#retval = dict(zip([filename], [img])), label, weight
retval = img, label, weight
return retval
num_files = len(filenames)
filenames = tf.constant(filenames)
#*********** Setup dataset below ************
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels, weights))
dataset=dataset.map(_parse_fn)
dataset = dataset.repeat()
dataset = dataset.batch(32)
iterator = dataset.make_initializable_iterator()
batch_features, batch_labels, batch_weights = iterator.get_next()
return {'images': batch_features, 'labels': batch_labels, 'weights': batch_weights}, iterator
def search_tf_table_for_entry(self, product_id):
'''Looks up keys in the table and outputs the values. Will return -1 if not found '''
if product_id is not None:
return self._products_to_class_table.lookup(product_id)
else:
if not self._real_eval:
logger().info("class not found in training {} ".format(product_id))
return -1
Where I create the model and have the placeholders used previously:
...
def create_model(self):
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)
biases_regularizer = weights_regularizer
# Input data.
self._input_images = tf.placeholder(
tf.float32, shape=(None, self._image_height, self._image_width, self._num_channels), name="ph_input_images")
self._input_labels = tf.placeholder(tf.int64, shape=(None, 1), name="ph_input_labels")
self._input_weights = tf.placeholder(tf.float32, shape=(None, 1), name="ph_input_weights")
self._is_training = tf.placeholder(tf.bool, name='ph_is_training')
self._keep_prob = tf.placeholder(tf.float32, name="ph_keep_prob")
self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, tf.float32))
...
self.create_summaries()
def create_summaries(self):
val_summaries = []
with tf.device("/cpu:0"):
for var in self._act_summaries:
self._add_act_summary(var)
for var in self._train_summaries:
self._add_train_summary(var)
self._summary_op = tf.summary.merge_all()
self._summary_op_val = tf.summary.merge(val_summaries)
def get_summary(self, sess, images, labels, weights):
feed_dict = {self._input_images: images, self._input_labels: labels,
self._input_weights: weights, self._is_training: False}
summary, acc = sess.run([self._summary_op, self._accuracy], feed_dict=feed_dict)
return summary, acc
Since the error says:
Cannot feed value of shape (32,) for Tensor 'ph_input_labels:0', which has shape '(?, 1)
My guess is your labels in get_summary has the shape [32]. Can you just reshape it to (32, 1)? Or maybe reshape the label earlier in _parse_fn?
Can someone please explain (or point me to the relevant place in the documentation that I've missed) how to properly update a tf.Variable() in a tf.while_loop? I am trying to update variables in the loop that will store some information until the next iteration of the loop using the assign() method. However, this isn't doing anything.
As the values of mu_tf and sigma_tf are being updated by the minimizer, while step_mu isn't, I am obviously doing something wrong, but I don't understand what it is. Specifically, I guess I should say that I know assign() does not do anything until it is executed when the graph is run, so I know that I can do
sess.run(step_mu.assign(mu_tf))
and that will update step_mu, but I want to do this in the loop correctly. I don't understand how to add an assign operation to the body of the loop.
A simplified working example of what I'm doing follows here:
import numpy as np
import tensorflow as tf
mu_true = 0.5
sigma_true = 1.5
n_events = 100000
# Placeholders
X = tf.placeholder(dtype=tf.float32)
# Variables
mu_tf = tf.Variable(initial_value=tf.random_normal(shape=[], mean=0., stddev=0.1,
dtype=tf.float32),
dtype=tf.float32)
sigma_tf = tf.Variable(initial_value=tf.abs(tf.random_normal(shape=[], mean=1., stddev=0.1,
dtype=tf.float32)),
dtype=tf.float32,
constraint=lambda x: tf.abs(x))
step_mu = tf.Variable(initial_value=-99999., dtype=tf.float32)
step_loss = tf.Variable(initial_value=-99999., dtype=tf.float32)
# loss function
gaussian_dist = tf.distributions.Normal(loc=mu_tf, scale=sigma_tf)
log_prob = gaussian_dist.log_prob(value=X)
negative_log_likelihood = -1.0 * tf.reduce_sum(log_prob)
# optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=0.1)
# sample data
x_sample = np.random.normal(loc=mu_true, scale=sigma_true, size=n_events)
# Construct the while loop.
def cond(step):
return tf.less(step, 10)
def body(step):
# gradient step
train_op = optimizer.minimize(loss=negative_log_likelihood)
# update step parameters
with tf.control_dependencies([train_op]):
step_mu.assign(mu_tf)
return tf.add(step,1)
loop = tf.while_loop(cond, body, [tf.constant(0)])
# Execute the graph
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step_loss = sess.run(fetches=negative_log_likelihood, feed_dict={X: x_sample})
print('Before loop:\n')
print('mu_tf: {}'.format(sess.run(mu_tf)))
print('sigma_tf: {}'.format(sess.run(sigma_tf)))
print('step_mu: {}'.format(sess.run(step_mu)))
print('step_loss: {}\n'.format(step_loss))
sess.run(fetches=loop, feed_dict={X: x_sample})
print('After loop:\n')
print('mu_tf: {}'.format(sess.run(mu_tf)))
print('sigma_tf: {}'.format(sess.run(sigma_tf)))
print('step_mu: {}'.format(sess.run(step_mu)))
print('step_loss: {}'.format(step_loss))
I would like to use the sparse_softmax_cross_entropy_with_logits
with the julia TensorFlow wrapper.
The operations is defined in the code here.
Basically, as I understand it the first argument should be logits, that would normally be fed to softmax to get them to be category probabilities (~1hot output).
And the second should be the correct labels as label ids.
I have adjusted the example code from the TensorFlow.jl readme
See below:
using Distributions
using TensorFlow
# Generate some synthetic data
x = randn(100, 50)
w = randn(50, 10)
y_prob = exp(x*w)
y_prob ./= sum(y_prob,2)
function draw(probs)
y = zeros(size(probs))
for i in 1:size(probs, 1)
idx = rand(Categorical(probs[i, :]))
y[i, idx] = 1
end
return y
end
y = draw(y_prob)
# Build the model
sess = Session(Graph())
X = placeholder(Float64)
Y_obs = placeholder(Float64)
Y_obs_lbl = indmax(Y_obs, 2)
variable_scope("logisitic_model", initializer=Normal(0, .001)) do
global W = get_variable("weights", [50, 10], Float64)
global B = get_variable("bias", [10], Float64)
end
L = X*W + B
Y=nn.softmax(L)
#costs = log(Y).*Y_obs #Dense (Orginal) way
costs = nn.sparse_softmax_cross_entropy_with_logits(L, Y_obs_lbl+1) #sparse way
Loss = -reduce_sum(costs)
optimizer = train.AdamOptimizer()
minimize_op = train.minimize(optimizer, Loss)
saver = train.Saver()
# Run training
run(sess, initialize_all_variables())
cur_loss, _ = run(sess, [Loss, minimize_op], Dict(X=>x, Y_obs=>y))
When I run it however, I get an error:
Tensorflow error: Status: Incompatible shapes: [1,100] vs. [100,10]
[[Node: gradients/SparseSoftmaxCrossEntropyWithLogits_10_grad/mul = Mul[T=DT_DOUBLE, _class=[], _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/SparseSoftmaxCrossEntropyWithLogits_10_grad/ExpandDims, SparseSoftmaxCrossEntropyWithLogits_10:1)]]
in check_status(::TensorFlow.Status) at /home/ubuntu/.julia/v0.5/TensorFlow/src/core.jl:101
in run(::TensorFlow.Session, ::Array{TensorFlow.Port,1}, ::Array{Any,1}, ::Array{TensorFlow.Port,1}, ::Array{Ptr{Void},1}) at /home/ubuntu/.julia/v0.5/TensorFlow/src/run.jl:96
in run(::TensorFlow.Session, ::Array{TensorFlow.Tensor,1}, ::Dict{TensorFlow.Tensor,Array{Float64,2}}) at /home/ubuntu/.julia/v0.5/TensorFlow/src/run.jl:143
This only happens when I try to train it.
If I don't include an optimise function/output then it works fine.
So I am doing something that screws up the gradient math.
i wanna display some python variables in tensorboard, but i dont get it done.
My code so far, display only a line in tensorboard for the lines with static number, if i use the outcommented-lines, it does not work ? It then prints:
ValueError: Shapes () and (?,) are not compatible
Someone has an idea?
import tensorflow as tf
step = 0
session = tf.Session()
tensorboardVar = tf.Variable(0, "tensorboardVar")
pythonVar = tf.placeholder("int32", [None])
#update_tensorboardVar = tensorboardVar.assign(pythonVar)
update_tensorboardVar = tensorboardVar.assign(4)
tf.scalar_summary("myVar", update_tensorboardVar)
merged = tf.merge_all_summaries()
sum_writer = tf.train.SummaryWriter('/tmp/train/c/', session.graph)
session.run(tf.initialize_all_variables())
for i in range(100):
_, result = session.run([update_tensorboardVar, merged])
#_, result = session.run([update_tensorboardVar, merged], feed_dict={pythonVar: i})
sum_writer.add_summary(result, step)
step += 1
this is working:
import tensorflow as tf
import numpy as np
step = 0
session = tf.Session()
tensorboardVar = tf.Variable(0, "tensorboardVar")
pythonVar = tf.placeholder("int32", [])
update_tensorboardVar = tensorboardVar.assign(pythonVar)
tf.scalar_summary("myVar", update_tensorboardVar)
merged = tf.merge_all_summaries()
sum_writer = tf.train.SummaryWriter('/tmp/train/c/', session.graph)
session.run(tf.initialize_all_variables())
for i in range(100):
#_, result = session.run([update_tensorboardVar, merged])
j = np.array(i)
_, result = session.run([update_tensorboardVar, merged], feed_dict={pythonVar: j})
sum_writer.add_summary(result, step)
step += 1
An alternative way can be found in the second answer to Computing exact moving average over multiple batches in tensorflow. There it is shown how you can create custom summaries.