Hello,

It would be great if you could add the project requirements (requirements.txt, or in some other form). Installing the required libraries is not enough, since we end up not having the correct library versions for running the code.

Thanks

I have installed rnn and tensorflow package but following error showing:

from rnn_class.brown import abget_sentences_with_word2idx_limit_voc, get_sentences_with_word2idx ModuleNotFoundError: No module named 'rnn_class'

There is some error in the code. Kindly check it and resolve it out as soon as possible. My MacOS terminal says: line 349 with open(f'{models_folder}/scaler.pkl', 'rb') as f: ^ SyntaxError: invalid syntax

Hello.

I bought "Convolutional Neural Networks in Python". And, cnn_tf.py (Chapter 5:Sample Code in Tensorflow) is worked.

Now, I have simple question. How shold I evaluate it ??? Please give me some advice if you do not mind.

thanks!!

Hi, @lazyprogrammer ,

Does it miss a line "(1, 1): step_cost," under line 104 in grid_world.py, according to only two terminal states such as (0, 3) and (1, 3)?

Should the linear_regression_class/moore.csv be replaced with tf2.0/moore.csv?

The linear_regression_class/moore.py file gives an error with python 3.8.10:

x = int(non_decmial.sub("", r[2].split("[")[0]))
IndexError: list index out of range

OR perhaps correcting this(Illegal quoting in line 88)?

"iterative_policy_evaluation" in the mentioned file must be changed to "iterative_policy_evaluation_deterministic" (or probabilistic).

I have been following the poetry generation notebook and at the point where we have to create an embedding matrix that uses the following code

# prepare embedding matrix
num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)
embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))
for word, i in word2idx.items():
    if i < MAX_VOCAB_SIZE:
        embedding_vector = word2vec.get(word)
        if embedding_vector is not None:
            # words not found in embedding index will be all zeros
            embedding_maxtrix[i] = embedding_vector

in the tutorial MAX_VOCAB_SIZE = 3000 and len(word2idx) + 1 = 4615. Shouldn't the second line be num_words = max(MAX_VOCAB_SIZE, len(word2idx) + 1) because if we take the min, we're essentially dropping out some words from the embedding matrix? As in we should be considering each and every word in word2idx. What really is the point of num_words here if we can just create the embedding matrix of size len(word2idx) + 1) * (EMBEDDING_DIM).

PyTorch: Deep Learning and Artificial Intelligence I just want to ask that your course "PyTorch: Deep Learning and Artificial Intelligence" is very expensive on Udemy. I like your lectures, and learned a lot from previous courses, Thank you!!

I am trying to run your linear_rl_trader.py file. but there is a problem with train.npy file. The problem is running but the result can not be saved.

Error: 'the path of my train.npy file' is not UTF-8 format

Do you know how I can solve this problem? I added encoding='utf-8' in the following but still it didn't solved.

np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)

I'm following the how to install guide of the data-science-linear-regression-in-python course.

The code for verifying that tensorflow is working correctly, didn't work for me, so I investigated how to fix it. Here is the error I was experiencing.

PROMPT> python3 tensorflow1.py 
Traceback (most recent call last):
  File "tensorflow1.py", line 20, in <module>
    A = tf.placeholder(tf.float32, shape=(5, 5), name='A')
AttributeError: module 'tensorflow' has no attribute 'placeholder'

On TF's website there is a migration guide.

Change from

import tensorflow as tf

Change to

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

This solved it for me.

Please bear with me here.

This might be confusing to understand for some because I'm adding the pseudocode to support what's unclear to me. I've been following a tutorial and it was mentioned that we need to define a for loop over the target sequences considering we're doing machine translation using attention mechanisms using LSTMs.

I've made it something that would look like Keras.

This is the pseudocode

h = encoder(input) # For getting input sequences for calculation attention weights and context vector
decoder_hidden_state = 0 # Hidden state
decoder_cell_state = 0 # Cell state
outputs = []

for t in range(Ty): # Ty being the length of the target sequence
    context = calc_attention(decoder_hidden_state, h) # decoder_hiddent_state(t-1), h(1),......h(Tx)
    decoder_output, decoder_hidden_state, decoder_cell_state = decoder_lstm(context, init = [decoder_hidden_state,decoder_cell_state])
    probabilities = dense_layer(o)
    outputs.append(probabilities)

model = Model ( input, outputs)

The thing that is unclear to me is why are we using a for loop, It was said that "In a regular seq2seq, we pass in the entire target input sequence all at once because the output was calculated all at once. But we need a loop over Ty steps since each context depends on the previous state"

But I think that the same can be done in the case of attention because if I just remove the for loop.

Just like this code below, which is the decoder part of a normal seq2seq

decoder_inputs_placeholder = Input(shape=(max_len_target,))
decoder_embedding = Embedding(num_words_output, EMBEDDING_DIM)
decoder_inputs_x = decoder_embedding(decoder_inputs_placeholder)
decoder_lstm = LSTM(
   LATENT_DIM,
   return_sequences=True,
   return_state=True,
)

If I want to add attention can't I just define the states here and call the calc_attention function that would return the context for a particular timestep while decoding, and can be passed onto the lstm call just as done before in pseudocode?

decoder_outputs, decoder_hidden_state, decoder_cell_state = decoder_lstm(
   decoder_inputs_x,
   initial_state=[decoder_hidden_state, decoder_cell_state]
)
decoder_outputs = decoder_dense(decoder_outputs)

There is a small typo in svm_class/svm_gradient.py.

Should read distribution rather than distrbution.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

it apperas that the accuracy of the model is so good but after saving the model and load it, it gives a non reasonable results .. the model is not saved correctly