Lab Materials for MIT 6.S191: Introduction to Deep Learning

Hey, Why am i having an error with this loc "import util.download_lung_data", is it correct?? I am getting an error of no module named util

Thanks

Hi amini, I'm trying to pull the docker images by this link DockerHub, it seems that there're some problems. I try docker pull mit6s191/iap2018 in terminal, it gives me this message:

Using default tag: latest
Error response from daemon: manifest for mit6s191/iap2018:latest not found

then, I use docker pull mit6s191/iap2018:labs, it shows:

error pulling image configuration: Get https://dseasb33srnrn.cloudfront.net/registry-v2/docker/registry/v2/blobs/sha256/48/48db1fb4c0b5d8aeb65b499c0623b057f6b50f93eed0e9cfb3f963b0c12a74db/data?Expires=1524752941&Signature=AKuwnCd69y-fs0NlLjQnAlBoUhbht-gWbIYIoIESf7dERzjlkeejUndYC1QCnEhjjlhZAvv2NWQFWEf-Efc6noGUV9hK4QRVaQqO23zRKRrqarTWVMLj5LQX4X1Qikze5YEXy4VqdNm5t88WRQsfDvsPHHDmKx6vqA2V4VgVDP8_&Key-Pair-Id=APKAJECH5M7VWIS5YZ6Q: net/http: TLS handshake timeout

Could you please help to address this problem? Thank you.

I'm getting this error in most of the notebooks. Stacktrace in Colab ->

ModuleNotFoundError Traceback (most recent call last) in () 14 15 # Import the necessary class-specific utility files for this lab ---> 16 import introtodeeplearning_labs as util

/content/introtodeeplearning_labs/init.py in () ----> 1 from lab1 import * 2 from lab2 import * 3 # from lab3 import * 4 5

ModuleNotFoundError: No module named 'lab1'

I ran the code below with colab, but I didn't hear a sound. !pip install mitdeeplearning import mitdeeplearning as mdl

songs = mdl.lab1.load_training_data() example_song = songs[0] mdl.lab1.play_song(example_song)

I am trying to try the notebooks posted here as per the instructions given in the readme here as:

sudo docker run -p 8888:8888 -p 6006:6006 -v https://github.com/aamini/introtodeeplearning_labs:/notebooks/introtodeeplearning_labs mit6s191/iap2018:labs

when I get the error:

docker: Error response from daemon: invalid bind mount spec "https://github.com/aamini/introtodeeplearning_labs:/notebooks/introtodeeplearning_labs": invalid mode: /notebooks/introtodeeplearning_labs.

Am I missing the path for the repo?

Regarding Section 2.2 of Lab2, Part2 - Debiasing, the training data hosted at https://www.dropbox.com/s/dl/bp54q547mfg15ze/train_face.h5 no longer exists. Thanks for the great class!

Hi, quick solution for importing this modules? ModuleNotFoundError Traceback (most recent call last) in () 8 from IPython import display as ipythondisplay 9 ---> 10 import introtodeeplearning_labs as util 11 12 is_correct_tf_version = '1.14.0' in tf.version

/content/introtodeeplearning_labs/init.py in () ----> 1 from lab1 import * 2 from lab2 import * 3 # from lab3 import * 4 5

Running lab1/Part2_music_generation.ipynb fails at the beginning because it requests 1.13.0. Looks like Colab updated to 1.13.1 (I didn't do anything special).

is_correct_tf_version = '1.13.0' in tf.__version__
...
AssertionError: Wrong tensorflow version (1.13.1) installed

Replacing that with this will work, unless that specific version is important:

is_correct_tf_version = tf.__version__ >= '1.13.0'

lab1/Part1_tensorflow_solution.ipynb includes this code:

def our_dense_layer(x, n_in, n_out):
  # ...
  z = tf.matmul(x,W) + b

When this is called in the next cell, it produces this warning:

WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py:642: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
tf.Tensor([[0.95257413 0.95257413 0.95257413]], shape=(1, 3), dtype=float32)

This warning can be avoided by replacing the +b code segment with tf.add, i.e.

z = tf.add(tf.matmul(x, W), b, name="z")

(also more TensorFlow-y)

I am encountering an InvalidArgumentErrorerror when I use Keras model.predict with tf.constant as input. I am not sure whether it's because model.predict don't work with tf.constant or I am doing something wrong. It work fine when I use numpy array with same argument.

# Define the number of inputs and outputs
n_input_nodes = 2
n_output_nodes = 3

# First define the model 
model = Sequential()

'''TODO: Define a dense (fully connected) layer to compute z'''
# Remember: dense layers are defined by the parameters W and b!
# You can read more about the initialization of W and b in the TF documentation :) 
dense_layer = Dense(n_output_nodes, input_shape=(n_input_nodes,),activation='sigmoid') # TODO 

# Add the dense layer to the model
model.add(dense_layer)

Now when I do prediction using:

# Test model with example input
x_input = tf.constant([[1.0,2.]], shape=(1,2))
'''TODO: feed input into the model and predict the output!'''
print(model.predict(x_input)) # TODO

I get following error:

InvalidArgumentError: In[0] is not a matrix. Instead it has shape [2] [[{{node MatMul_3}}]] [Op:StatefulPartitionedCall]

When I use Numpy array, it works:

# Test model with example input
x_input =np.array([[1.0,2.]])
'''TODO: feed input into the model and predict the output!'''
print(model.predict(x_input)) # TODO

[[0.19114174 0.88079417 0.8062956 ]]

Could you let me know if it is a tf issue. If so I can raise an issue on the tf repository.

I'm doing the pong part of RL Lab I ran into this issue i've tried looking into all versions of mitdeeplearning i didn't find pong_change function can you fix this

Maybe this gets a little bit late, but I think this is also a good chance to say thank you to Alex and Ava for this great course.

Here is my questions:

When I went through Music_Generation codes, the answer here was quite confusing to me. Only one character passes to the model each time although it is updating but the previous information is missing. (I think this also part the reason why the songs generated are always invalid.)

~Pass the prediction along with the previous hidden state ~as the next inputs to the model input_eval = tf.expand_dims([predicted_id], 0)

So I save the initial input and concatenate with each output as the next input. This makes more sense to me and the results start to be much better, but I'm not sure if I make something wrong or there are better ways, like taking the previous state as the next initializer state.

out_eval = tf.expand_dims([predicted_id], 0) input_eval = tf.concat([input_eval, output_eval], 1)

The last matrix defined by the user in section 1.1 has to be a 2-d Tensor. Thus, it'll have a shape of (n, 2) or (2, n). The code in the Lab 1 notebook has:

row_vector = matrix[1]
column_vector = matrix[:,2]
scalar = matrix[1, 2]

This assumes the column-rank to be >=3. IMHO, it should instead be:

row_vector = matrix[1]
column_vector = matrix[:,1]
scalar = matrix[0, 1]

And assume the user will choose a (2,2) matrix as their solution.

Hello, I have a question on test set bias of the standard CNN model. After running cell #13 in Part2_Debiasing.ipynb, I see this histogram:

Am I correct in interpreting that avg test set accuracy is ~65% for Light Female, ~70% for Light Male, ~82% for Dark Female, and ~90% for Dark Male?

If training set has majority of data on light skinned female, I expected test set accuracy to be higher for that category. So, results of above hisogram are counter intuitive.

What am I missing?

--Rahul

Getting the following output when attempting to install mitdeeplearning via pip on an M1 mac:

% pip install mitdeeplearning
DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621
Collecting mitdeeplearning
  Using cached mitdeeplearning-0.2.0.tar.gz (2.1 MB)
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy in /opt/homebrew/lib/python3.9/site-packages (from mitdeeplearning) (1.22.3)
Collecting regex
  Downloading regex-2022.3.15-cp39-cp39-macosx_11_0_arm64.whl (281 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 281.8/281.8 KB 5.1 MB/s eta 0:00:00
Collecting tqdm
  Using cached tqdm-4.64.0-py2.py3-none-any.whl (78 kB)
Collecting gym
  Downloading gym-0.23.1.tar.gz (626 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 626.2/626.2 KB 17.9 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting mitdeeplearning
  Downloading mitdeeplearning-0.1.2.tar.gz (2.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 45.2 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
  Downloading mitdeeplearning-0.1.1.tar.gz (2.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 37.5 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
  Downloading mitdeeplearning-0.1.0.tar.gz (2.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 36.1 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
ERROR: Cannot install mitdeeplearning==0.1.0, mitdeeplearning==0.1.1, mitdeeplearning==0.1.2 and mitdeeplearning==0.2.0 because these package versions have conflicting dependencies.

The conflict is caused by:
    mitdeeplearning 0.2.0 depends on tensorflow>=2.0.0a
    mitdeeplearning 0.1.2 depends on tensorflow>=2.0.0a
    mitdeeplearning 0.1.1 depends on tensorflow>=2.0.0a
    mitdeeplearning 0.1.0 depends on tensorflow>=2.0.0a

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

ERROR: Could not find a version that satisfies the requirement tensorflow>=2.0.0a 

Looking for a solution that allows the installation of this package. I'm using python3.9 and I have tensorflow-macos installed.

During running the following section in lab 3 on autonomous driving:

import vista from vista.utils import logging logging.setLevel(logging.ERROR)

I get an error:

::WARNING::[vista.entities.sensors.EventCamera.] Fail to import module for event camera. Remember to do source /openeb/build/utils/scripts/setup_env.shCan ignore this if not using it

How to solve this problem?

I try to continue , then in the following code section: camera = car.spawn_camera(config={'size': (200, 320)})

I got GL error: GLError: GLError( err = 12290, baseOperation = eglMakeCurrent, cArguments = ( <OpenGL._opaque.EGLDisplay_pointer object at 0x7ff7681b5b90>, <OpenGL._opaque.EGLSurface_pointer object at 0x7ff768c97dd0>, <OpenGL._opaque.EGLSurface_pointer object at 0x7ff768c97dd0>, <OpenGL._opaque.EGLContext_pointer object at 0x7ff767f89290>, ), result = 0 )

I use in Google Colab (and GPU) .