1553330966
Tensorflow: Logits and labels must have the same first dimension
I’m new to machine learning in TF. I have this dataset which I generated and exported into a .csv file. It is here: tftest.csv.
The ‘distributions’ column corresponds to a unique system of equations which I have tried to condense down into a series of digits in SageMath. The ‘probs’ column correspond to whether one should mutiply a given equation by a given monomial of the equation, based on the row and column it is located in. The above is just for overview and is not related to my actual question.
Anyways, here’s my code. I’ve tried to explain it as best as I can with annotations.
import csv
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras as keras
distribution_train = []
probs_train = []
# x_train = []
# y_train = []
with open('tftest.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
distribution_train.append(row[0])
probs_train.append(row[1])
'''
Get rid of the titles in the csv file
'''
distribution_train.pop(0)
probs_train.pop(0)
'''
For some reason everything in my csv file is stored as strings.
The below function is to convert it into floats so that TF can work with it.
'''
def num_converter_flatten(csv_list):
f = []
for j in range(len(csv_list)):
append_this = []
for i in csv_list[j]:
if i == '1' or i == '2' or i == '3' or i == '4' or i == '5' or i == '6' or i == '7' or i == '8' or i =='9' or i =='0':
append_this.append(float(i))
f.append((append_this))
return f
x_train = num_converter_flatten(distribution_train)
y_train = num_converter_flatten(probs_train)
x_train = tf.keras.utils.normalize(x_train, axis=1)
y_train = tf.keras.utils.normalize(y_train, axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
'''
I'm making the final layer 80 because I want TF to output the size of the
'probs' list in the csv file
'''
model.add(tf.keras.layers.Dense(80, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
However, when I run my code, I get the following error.
tensorflow.python.framework.errors_impl.<span class="hljs-symbol">InvalidArgumentError:</span> logits <span class="hljs-keyword">and</span> labels must have the same first dimension, got logits shape [<span class="hljs-number">32</span>,<span class="hljs-number">80</span>] <span class="hljs-keyword">and</span> labels shape [<span class="hljs-number">2560</span>]
[[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWi
I searched online for this error, but I can’t seem to understand why it’s cropping up. Can anyone help me understand what’s wrong with my code? If there are any questions as well, please leave a comment and I’ll do my best to answer them.
#python #tensorflow #machine-learning
What is GEEK
Buddha Community
1548131106
Your logits shape looks right, batch size of 3, and output layer of size 2, which is what you defined as your output layer. Your labels should be shape [3, 2] also. Batch of 3, and each batch has 2 [1,0] or [0,1].
Also note that when you have a boolean classification output you shouldn’t have 2 neurons on the output/logits layer. You can just output a single value which takes on 0 or 1, you can probably see how 2 outputs of [1,0], and [0,1] is redundant and can be expressed as a simple [0|1] value. Also you tend to get better results when you do it this way.
Thus, your logits should end up being [3,1] and your labels should be an array of 3 values, one for each of the samples in your batch.
1571625497
Thx
1553330966
Tensorflow: Logits and labels must have the same first dimension
I’m new to machine learning in TF. I have this dataset which I generated and exported into a .csv file. It is here: tftest.csv.
The ‘distributions’ column corresponds to a unique system of equations which I have tried to condense down into a series of digits in SageMath. The ‘probs’ column correspond to whether one should mutiply a given equation by a given monomial of the equation, based on the row and column it is located in. The above is just for overview and is not related to my actual question.
Anyways, here’s my code. I’ve tried to explain it as best as I can with annotations.
import csv
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras as keras
distribution_train = []
probs_train = []
# x_train = []
# y_train = []
with open('tftest.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
distribution_train.append(row[0])
probs_train.append(row[1])
'''
Get rid of the titles in the csv file
'''
distribution_train.pop(0)
probs_train.pop(0)
'''
For some reason everything in my csv file is stored as strings.
The below function is to convert it into floats so that TF can work with it.
'''
def num_converter_flatten(csv_list):
f = []
for j in range(len(csv_list)):
append_this = []
for i in csv_list[j]:
if i == '1' or i == '2' or i == '3' or i == '4' or i == '5' or i == '6' or i == '7' or i == '8' or i =='9' or i =='0':
append_this.append(float(i))
f.append((append_this))
return f
x_train = num_converter_flatten(distribution_train)
y_train = num_converter_flatten(probs_train)
x_train = tf.keras.utils.normalize(x_train, axis=1)
y_train = tf.keras.utils.normalize(y_train, axis=1)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
'''
I'm making the final layer 80 because I want TF to output the size of the
'probs' list in the csv file
'''
model.add(tf.keras.layers.Dense(80, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
However, when I run my code, I get the following error.
tensorflow.python.framework.errors_impl.<span class="hljs-symbol">InvalidArgumentError:</span> logits <span class="hljs-keyword">and</span> labels must have the same first dimension, got logits shape [<span class="hljs-number">32</span>,<span class="hljs-number">80</span>] <span class="hljs-keyword">and</span> labels shape [<span class="hljs-number">2560</span>]
[[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWi
I searched online for this error, but I can’t seem to understand why it’s cropping up. Can anyone help me understand what’s wrong with my code? If there are any questions as well, please leave a comment and I’ll do my best to answer them.
#python #tensorflow #machine-learning
1623704506
White Label Studio Review & Bonuses
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1616751555
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#white label crypto exchange software #white label bitcoin exchange software #white label bitcoin exchange #white label crypto exchange #white label cryptocurrency exchange software
1623228736
5 Steps to Passing the TensorFlow Developer Certificate
Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!
- Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.
2. Do the course questions in parallel in PyCharm.
…
#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing
1599921480
API-First, Mobile-First, Design-First... How Do I Know Where to Start?
Dear Frustrated,
I understand your frustration and I have some good news and bad news.
Bad News First (First joke!)
- Stick around another 5-10 years and there will be plenty more firsts to add to your collection!
- Definitions of these Firsts can vary from expert to expert.
- You cannot just pick a single first and run with it. No first is an island. You will probably end up using a lot of these…
Good News
While there are a lot of different “first” methodologies out there, some are very similar and have just matured just as our technology stack has.
Here is the first stack I recommend looking at when you are starting a new project:
1. Design-First (Big Picture)
Know the high-level, big-picture view of what you are building. Define the problem you are solving and the requirements to solve it. Are you going to need a Mobile app? Website? Something else?
Have the foresight to realize that whatever you think you will need, it will change in the future. I am not saying design for every possible outcome but use wisdom and listen to your experts.
2. API First
API First means you think of APIs as being in the center of your little universe. APIs run the world and they are the core to every (well, almost every) technical product you put on a user’s phone, computer, watch, tv, etc. If you break this first, you will find yourself in a world of hurt.
Part of this First is the knowledge that you better focus on your API first, before you start looking at your web page, mobile app, etc. If you try to build your mobile app first and then go back and try to create an API that matches the particular needs of that one app, the above world of hurt applies.
Not only this but having a working API will make design/implementation of your mobile app or website MUCH easier!
Another important point to remember. There will most likely be another client that needs what this API is handing out so take that into consideration as well.
3. API Design First and Code-First
I’ve grouped these next two together. Now I know I am going to take a lot of flak for this but hear me out.
Code-First
I agree that you should always design your API first and not just dig into building it, However, code is a legitimate design tool, in the right hands. Not everyone wants to use some WYSIWYG tool that may or may not take add eons to your learning curve and timetable. Good Architects (and I mean GOOD!) can design out an API in a fraction of the time it takes to use some API design tools. I am NOT saying everyone should do this but don’t rule out Code-First because it has the word “Code” in it.
You have to know where to stop though.
Designing your API with code means you are doing design-only. You still have to work with the technical and non-technical members of your team to ensure that your API solves your business problem and is the best solution. If you can’t translate your code-design into some visual format that everyone can see and understand, DON’T use code.
#devops #integration #code first #design first #api first #api