Padma Aon Prakasha Famous Books

1: The power of the shakti

Padma Aon Prakasha: Shakti is the feminine life force that manifests, creates and activates constantly.
Ignoring this living power in us is the key to getting men and women to achieve their goals.
vital harmony, strengthened peace and sacred union. Unite the forms of Tantra Yoga
in sacred Indian, Tibetan and Hebrew traditions, Padma Aon reveals how
activate the power of Shakti by opening our 18 energy channels. Focused on
uterus in women and hara in humans, Shakti connects soul, body, emotions and
creative sexual flow in powerful experiential openings.

2: The nine eyes of light

Padma Aon Prakasha: We live in a time when many of us know other realities. Deeply
relevant to what we are experiencing today are key elements of the Egyptian tradition,
which served as a basis for their awakened civilization. Now, Padma Aon
brings the nine Egyptian keys of the light body to our time so that we can learn
Egyptian masters how to live, create and move our consciousness
multi-dimensionally. These nine keys are one of the oldest teachings of the
Light body work of a civilization that had mastered it.

3: The plan of Christ

Padma Aon Prakasha: The Master Plan of Christ is an inspiring card that was given to us by the apostles of Christ.
on the 13 qualities of Christ's consciousness. Revealed in communion with the
Masters of the Council of Christ of the first world church in Maries de la Mer
France, this book that opens the eyes and opens the heart offers a deeper connection with
Christ in you.

4: The sacred wounds: the original innocence

Padma Aon Prakasha: The wounds of our soul are our path to our original innocence. Our wounds are
our own unique and perfect design, a synchronous sacred pattern that takes us backwards without
fail, in our own sovereign soul. Our original innocence is our purity, to which we return
in after having fully felt all our pleasures and human pains, joys and sufferings, emotions
and ecstasies. It is by fully embracing our humanity through the path of life that we
have the opportunity to enter our twice born innocence.

5: Dimensions of love

Padma Aon Prakasha: 7 steps towards God brings new and sometimes radical truths to the soul in the 21st century.
In the journey of souls, we all go through 7 spheres or dimensions. Each sphere has clear
signs, lessons, opportunities and emotions that show us where we really are in our journey of souls,
where we have to go and how to get there. Sufis, Christ and Saint Teresa of Avila shared
these seven steps, and in Dimensions of Love, they come together to help us see what that means.
take for us to wake up today.

6: the wisdom of the uterus

Padma Aon Prakasha: Voted one of the top 30 books to read, Womb Wisdom is the pioneer who catalyzed
the current resurgence of uterine-based practices and spirituality. In the past and in the present
Native traditions, women have known that the woman's womb houses the greatest power that a woman
possesses: the power to create at all levels. This power can also be exploited in the birth of
projects, careers, deep healing, spiritual growth and deeper relationships. The uterus is the
a link between women and the web of life, and we can relearn how to exploit this
immense creative potential to revolutionize the way we live, heal and create.

7: The sacred relationships

Padma Aon Prakasha: We live at a time on our planet where we are called to reach the deepest of us
the courage to undertake an incredibly stimulating and incredible creative act: the birth
of the divine man. This genesis is happening all over the world: not just in humans,
but in all life and the Earth itself. We believe that this change is the answer to our crisis and to the
the deepest sense of it. The birth of the divine human is the key to our evolutionary destiny,
and that goes through the sacred relationship.

.

Amazon Web Services – Removing and then reinstalling Anaconda on an AWS Ubuntu Deep Learning EC2 instance and unable to enter in-depth learning environments

I just set up an Ubuntu Deep Learning AMI EC2 instance. I am a beginner on AWS / Packet Processing.

My goal is to use the instance to run a Python deep learning script. This script uses a variety of packages.

When installing some of these packages with conda, an error has occurred indicating inconsistencies in the environment for more than 100 packages. After several attempts to solve this problem, I thought that removing Anaconda and reinstalling it could do the trick. After that, I realized that I had perhaps further spoiled my instance. I can no longer use the predefined deep learning environments for which the AMI has been configured because they have been accessed using conda commands, which seems to have been removed (IMO).

I've tried repeating the commands, but I get an error stating that these environments no longer exist. A tutorial using these commands is mentioned here:
https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-conda.html

active source tensorflow_p36

I was expecting the above to enter the tensorflow_p36 environment. A sin:

(tensorflow_p36) ubuntu @ ip-172-31-45-96: ~ / scripts

However, this gives an error message:

impossible to find the environment: tensorflow_p36

I realize that the uninstallation of conda was a major rookie error that seems to have totally disabled my instance. If anyone has any ideas to get it back, it would be very appreciated!

thank you so much

python – deep learning model eras flask api: ValueError: tensor tensor ("softmax / softmax: 0", shape = (?, 2), dtype = float32) is not an element of this graph

I use keras, flask to develop a transfer learning model with a web API to predict the tag of an image. Since there are about 10 tags in an image. I have to load each tag template to predict the tags. I want to load the model before the json request because loading the keras model takes a lot of time. but there is an error when I used the loaded template to predict the image tags after a json query.

my code:

img_dim = (299, 299, 3)
img_size = (299, 299)
num_label = 2

image_template = load_resnet_model ()

industry = 100 & # 39;
lst_model = []

for t_image in lst_main_image:
lst_model = []
    ml = image template
for i in the range (1, 6):
ml.load_weights (& # 39; ../model/ {} / main_image / {} _ aug_inception.fold _ {} {}. hdf5 .format (industry, industry, i, t_image))
ml.compile (optimizer = Adam (lr = 1e-4), loss = binary_crossentropy, metric =['accuracy'])
lst_model.append (ml)
print ('end the image:', t_image)
dict_model[t_image] = lst_model


def load_resnet_model ():

img_dim = (299, 299, 3)
img_size = (299, 299)
num_label = 2
print (& # 39; starts to get the pattern & # 39;)
input_tensor = Input (shape = img_dim)
base_model = InceptionResNetV2 (include_top = False, input_shape = img_dim, weights = imagenet & # 39;)
x = tensor of entry
x = Lambda (preprocess_input, name = "pretreatment") (x)
x = base model (x)
x = GlobalAveragePooling2D () (x)
x = abandonment (0.5) (x)
x = dense (num_label, activation = & # 39; softmax & # 39; name = & # 39; softmax & # 39;) (x)
model_image = Model (input_tensor, x)
print (finishing of the loading template)

return model_image

@ app.route ("/ api /", methods =["POST"])
def predict_tag ():
print (& # 39; begins to predict & # 39;)
# model_image = load_resnet_model (img_dim, num_label)
len_test = validation_batch.shape[0]
    for t in lst_main_image:
n_fold = 5
preds_test = np.zeros ((len_test, 2), dtype = np.float)
print (& # 39; t_image: & # 39; t)
tag_i_time = time.time ()
lst_t_model = dict_model
        for m in lst_t_model:
test_prob = m.predict (validation_batch)
preds_test + = test_prob
tag_i_e = time.time ()
print (& # 39; each tag the times: & # 39 ;, t, tag_i_e - tag_i_time)
preds_test / = n_fold
y_pred = preds_test.argmax (axis = -1)
lst_result_image.append (list (y_pred))
print (finish predicting the tag: 't')

But when I put the loading model in the Predict_tag function, this type of error does not exist. I want to put the loading model before sending the request.

127.0.0.1 - - [09/Jul/2019 15:15:38] "POST / API / HTTP / 1.1" 500 -
Traceback (last most recent call):
File "/anaconda3/lib/python3.6/site-packages/flask/app.py", line 1997, in __call__
return self.wsgi_app (about, start_response)
File "/anaconda3/lib/python3.6/site-packages/flask/app.py", line 1985, in wsgi_app
response = self.handle_exception (e)
"/Anaconda3/lib/python3.6/site-packages/flask/app.py" file, line 1540, in handle_exception
relaunch (exc_type, exc_value, tb)
"/Anaconda3/lib/python3.6/site-packages/flask/_compat.py" file, line 33, pending
increase value
File "/anaconda3/lib/python3.6/site-packages/flask/app.py", line 1982, in wsgi_app
response = self.full_dispatch_request ()
"/Anaconda3/lib/python3.6/site-packages/flask/app.py" file, line 1614, in full_dispatch_request
rv = self.handle_user_exception (e)
"/Anaconda3/lib/python3.6/site-packages/flask/app.py" file, line 1517, in handle_user_exception
relaunch (exc_type, exc_value, tb)
"/Anaconda3/lib/python3.6/site-packages/flask/_compat.py" file, line 33, pending
increase value
"/Anaconda3/lib/python3.6/site-packages/flask/app.py" file, line 1612, in full_dispatch_request
rv = self.dispatch_request ()
"/Anaconda3/lib/python3.6/site-packages/flask/app.py" file, line 1598, in dispatch_request
returns self.view_functions[rule.endpoint](** req.view_args)
"/Users/k.den/PycharmProjects/Banner-Tag-API/src/app792.py" file, line 188, in Predict_tag
test_prob = m.predict (validation_batch)
File "/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1164, in Predict
self._make_predict_function ()
"/Anaconda3/lib/python3.6/site-packages/keras/engine/training.py" file, line 554, in _make_predict_function
** kwargs)
File "/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2744, based
Return function (entries, exits, updates = updates, ** kwargs)
File "/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2546, in __init__
with tf.control_dependencies (self.outputs):
"/Anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py" file, line 5028, in control_dependencies
return get_default_graph (). control_dependencies (control_inputs)
"/Anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py" file, line 4528, in control_dependencies
c = self.as_graph_element (c)
"/Anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py" file, line 3478, in as_graph_element
return self._as_graph_element_locked (obj, allow_tensor, allow_operation)
"/Anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py" file, line 3557, in _as_graph_element_locked
raise ValueError ("The% s tensor is not an element of this graphic."% obj)

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tensorflow – Regardless of which graph contains a deep learning network (no network function available), what is the best way to create a fine tuned pipeline?

For example, I have the template function for inception_v3. I would like to modify the network by calling an external API (for example, compression / quantization) that would make me a modified graph. What is the best practice to adjust this network in a distributed way? Or more generally, what is the best practice to refine any network for which only a graph is given in tensorflow?

I am currently looking for tf-slim. To do this, distributed optimization consists of creating clones of the original network by allowing the sharing of variables and deploying clones (containing only operators) on GPUs. However, without knowing the template function, I can not think of a simple way to make it work. All suggestions will be greatly appreciated!

c # – I want to do sound recognition with deep learning

The purpose of this software would be to analyze video files and mark a timestamp in the file where the sample sound is found.

The software would take audio files and train with deep learning of this data. It would then identify other sounds similar to the transmitted video files.

My question is: how can I start? I have experience in C, C ++, C # and Python (eager to learn). I would like some suggestions, thanks.

deep learning – predicting time series with variable input length

My thesis focuses on the prediction of cancer in mice. I collected data from 35 mice. I measure the volume of tumors every day after the onset of cancer until the death of the mouse. The time of death varies between 50 and 72 days and so I have a series of 35 episodes of different length.

I have to predict the evolution of tumor volume over time. I want to use regression but I do not know how to adapt a model to 35 time series of different lengths.

note that I can not change the length of my dataset because I am losing important information about cancer behavior.

a suggestion for my problem?

java – What naming convention should you use for the 2 model classes of the same business logic, a shallow one and the other deep

In one of our services, we have business objects and we sometimes want to return them in their "flat" form and sometimes in their hierarchical form.

An example:

Let's say we have an object counts. Each account has a list of projects. Each project has a list of tasks.

Account:

Project:

  • login
  • first name
  • deadline
  • created at
  • Account ID

Task:

  • login
  • first name
  • created at
  • the description
  • projectId

When searching for an account (for example by username), we sometimes want to obtain "superficial" account data:

ShallowAccount {
String ID;
String name
String createdAt;
listing projectIds;
}

and sometimes the complete hierarchy:

Account {
String ID;
String name
String createdAt;
listing projects;
}

Where project:

Project {
String ID;
String name
String createdAt;
Chain deadline;
String accountId;
listing Tasks;
}

and the "shallow" project is:
ShallowProject:

Project {
String ID;
String name
Chain deadline;
String createdAt;
String accountId;
listing Chain;
}

and so on for Task.

Now for the question:

What is the naming convention for such objects (that is, shallow and complete objects)?