I am trying to train a LSTM using glove embeddings After reading through https://github.com/tflearn/tflearn/issues/8, I tried implementing it using the following code:

Reading and pre-processing data

Setting up an LSTM and training

but am getting the following error:

I followed all the instructions that were highlighted in https://github.com/tflearn/tflearn/issues/8 and I do not understand why it is crashing. Can someone please help? :(

Thanks a lot!

Hi,

I saw the example code of using LSTM cells in TFLearn, classifying mnist images. I want to classify an image sequence. I'm not sure though how to achieve this in TFLearn, but I thought maybe to go for this architecture:

1. Conv
2. Max Pooling .... repeat
3. Fully connected
4. LSTM
5. SoftMax

From what I understand normal layers receive this shape type: (batch_size, height, width, depth) while LSTM: (batch_size, sequence_length, input_length)

I thought of reshaping the input from: (batch_size, height, width, depth) to: (batch_size, sequence_length, height * width * depth)

but i'm not sure this how this should work. Am I missing something? Any advice?

After installing tflearn in windows 10.

For the line of code:

import tflearn

I got the error:

Traceback (most recent call last):
  File "C:\Users\Ernest\git\test-code\test-code\src\tflearn_quick_start\main.py", line 5, in <module>
    import tflearn
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tflearn\__init__.py", line 4, in <module>
    from . import config
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tflearn\config.py", line 5, in <module>
    from .variables import variable
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tflearn\variables.py", line 7, in <module>
    from tensorflow.contrib.framework.python.ops import add_arg_scope as contrib_add_arg_scope
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\__init__.py", line 30, in <module>
    from tensorflow.contrib import factorization
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\factorization\__init__.py", line 24, in <module>
    from tensorflow.contrib.factorization.python.ops.gmm import *
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\factorization\python\ops\gmm.py", line 27, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import estimator
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\__init__.py", line 87, in <module>
    from tensorflow.contrib.learn.python.learn import *
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\__init__.py", line 23, in <module>
    from tensorflow.contrib.learn.python.learn import *
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\__init__.py", line 25, in <module>
    from tensorflow.contrib.learn.python.learn import estimators
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\__init__.py", line 297, in <module>
    from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\dnn.py", line 29, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\dnn_linear_combined.py", line 31, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import estimator
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\estimator.py", line 49, in <module>
    from tensorflow.contrib.learn.python.learn.learn_io import data_feeder
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\learn_io\__init__.py", line 21, in <module>
    from tensorflow.contrib.learn.python.learn.learn_io.dask_io import extract_dask_data
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\tensorflow\contrib\learn\python\learn\learn_io\dask_io.py", line 26, in <module>
    import dask.dataframe as dd
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\dask\dataframe\__init__.py", line 3, in <module>
    from .core import (DataFrame, Series, Index, _Frame, map_partitions,
  File "C:\Users\Ernest\AppData\Local\Continuum\lib\site-packages\dask\dataframe\core.py", line 36, in <module>
    pd.computation.expressions.set_use_numexpr(False)
**AttributeError: module 'pandas' has no attribute 'computation'**

I use the following versions:

TensorFlow: 1.1.0-rc2
pandas: 0.20.1

Any help?

Running the following file throws an error: https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py

ValueError: Shape (128, ?) must have rank at least 3

Setup:

• MacOS Sierra (10.12)
• Python 2.7
• Tensorflow v1.2.0
• TFLearn v0.3.2

Traceback (most recent call last):
  File "bidirectional_lstm.py", line 47, in <module>
    net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128))
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tflearn/layers/recurrent.py", line 374, in bidirectional_rnn
    dtype=tf.float32)
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 375, in bidirectional_dynamic_rnn
    time_major=time_major, scope=fw_scope)
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 574, in dynamic_rnn
    dtype=dtype)
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 637, in _dynamic_rnn_loop
    for input_ in flat_input)
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/rnn.py", line 637, in <genexpr>
    for input_ in flat_input)
  File "/Users/colin/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.py", line 649, in with_rank_at_least
    raise ValueError("Shape %s must have rank at least %d" % (self, rank))
ValueError: Shape (128, ?) must have rank at least 3

I'm attempting to use conv2d_transpose in my network, but can't seem to get past this error: ValueError: output_shape does not match filter's output channels, 1 != 16. I'm wondering if its possible there is an issue in tflearn. I noticed that the tensorflow docs say the 3rd argument of conv2d_transpose is the output_shape, but tflearn passes in stride as the third argument.

Thanks in advance for your help.

Relevant bit of my network:

network = conv_2d(network, 4096, 6, activation='relu')
network = dropout(network, 0.5)
network = conv_2d(network, 4096, 1, activation='relu')
network = dropout(network, 0.5)
network = conv_2d(network, 16, 1)
network = conv_2d_transpose(network, 16, 63, strides=32)

Full traceback:

Traceback (most recent call last):
  File "ux_network.py", line 50, in <module>
    network = input_data(shape=[None, 224, 224, 3], data_preprocessing=img_prep)
  File "/usr/local/lib/python2.7/dist-packages/tflearn/layers/conv.py", line 175, in conv_2d_transpose
    inference = tf.nn.conv2d_transpose(incoming, W, strides, padding)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 93, in conv2d_transpose
    "{} != {}".format(output_shape[3], filter.get_shape()[2]))
ValueError: output_shape does not match filter's output channels, 1 != 16

Hi guys, I'm facing with this error right now. Not sure how to fix it, because this is the first time I check out TFLearn - Quick Start.

 [ ~/Desktop ] 👉 python my_test.py Traceback (most recent call last): File "my_test.py", line 7, in from tflearn.datasets import titanic ImportError: cannot import name titanic

I'm running a mac (10.11.6) and follow the installation tutor already. Thanks

I'm attempting to do this but the predicted values (in green) don't look anything like a sine wave.

I've tried various learning rates, optimizers, number of units, length of history for training, number of steps I try to look ahead. I'm not yet seeing what the problem is, so I would be grateful for suggestions.

# Simple example using recurrent neural network to predict time series values

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.normalization import batch_normalization
import numpy as np
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

step_radians = 0.01
steps_of_history = 100
steps_in_future = 2
index = 0

x = np.arange(0, 4*math.pi, step_radians)
y = np.sin(x)

# Put the data into the right shape
while (index+steps_of_history+steps_in_future < len(y)):
    window = y[index:index+steps_of_history]
    target = y[index+steps_of_history+steps_in_future]
    if index == 0:
        trainX = window
        trainY = target
    else:
        trainX = np.vstack([trainX, window])
        trainY = np.append(trainY, target)
    index = index+1
trainX.shape = (index, steps_of_history, 1)
trainY.shape = (index, 1)

# Network building
net = tflearn.input_data(shape=[None, steps_of_history, 1])
net = tflearn.simple_rnn(net, n_units=512, return_seq=False)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, 1, activation='linear')
net = tflearn.regression(net, optimizer='sgd', loss='mean_square', learning_rate=0.001)

# Training
model = tflearn.DNN(net, clip_gradients=0.0, tensorboard_verbose=0)
model.fit(trainX, trainY, n_epoch=150, validation_set=0.1, show_metric=True, batch_size=128)

# Prepare the testing data set
# testX = window to use for prediction
# testY = actual value
# predictY = predicted value
index = 0
while (index+steps_of_history+steps_in_future < len(y)):
    window = y[index:index+steps_of_history]
    target = y[index+steps_of_history+steps_in_future]
    if index == 0:
        testX = window
        testY = target
    else:
        testX = np.vstack([testX, window])
        testY = np.append(testY, target)
    index = index+1
testX.shape = (index, steps_of_history, 1)
testY.shape = (index, 1)

# Predict the future values
predictY = model.predict(testX)

# Plot the results
plt.figure(figsize=(20,4))
plt.suptitle('Prediction')
plt.title('History='+str(steps_of_history)+', Future='+str(steps_in_future))
plt.plot(y, 'r-', label='Actual')
plt.plot(predictY, 'gx', label='Predicted')
plt.legend()
plt.savefig('sine.png')

I tried to modify imdb example to my dataset, which is given below 3 3 373 27 9 615 9 16 10 34 0 8 0 199 65917 1319 122 402 319 183 3 3 77 12 4 66 4 3 0 5 0 14 3 50 106 139 38 164 53 109 3 3 86 6 2 6 2 0 0 1 0 25 0 4 284 77888 19 66 11 25 3 3 469 21 7 291 7 43 15 82 0 207 0 181 115646 59073 294 928 112 675 3 3 2090 21 7 4035 7 17 8 40 0 317 10 717 1033 25661 142 2054 1795 1023 3 3 691 18 6 597 6 30 16 61 0 245 18 273 719 2352305 213 1106 324 719 6 6 229 0 8 526 0 11 1 13 0 6 5 101 7246 2082 120 141 288 1570 3 3 1158 9 3 649 3 16 6 17 1 247 38 477 592 987626 82 1305 653 707 4 4 211 0 10 429 0 16 9 20 0 3 0 106 42725 27302 4280 133 477 1567

The first column is the target which has 9 classes and around 1803 features

from future import print_function import numpy as np from sklearn.cross_validation import train_test_split import tflearn import pandas as pd from tflearn.data_utils import to_categorical, pad_sequences

print("Loading") data = pd.read_csv('Train.csv')

X = data.iloc[:,1:1805] y = data.iloc[:,0]

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2,random_state=42)

print(X_train.shape) print(X_test.shape) print(y_train.shape) print(y_test.shape)

print("Preprocessing") X_train1 = X_train.values.T.tolist() X_test1 = X_test.values.tolist() y_train1 = y_train.values.T.tolist() y_test1 = y_test.values.tolist()

Data preprocessing

Sequence padding

trainX = pad_sequences(X_train1, maxlen=200, value=0.) testX = pad_sequences(X_test1, maxlen=200, value=0.)

Converting labels to binary vectors

trainY = to_categorical(y_train, nb_classes=0) testY = to_categorical(y_test, nb_classes=0)

Network building

net = tflearn.input_data([None, 200]) net = tflearn.embedding(net, input_dim=20000, output_dim=128) net = tflearn.lstm(net, 128) net = tflearn.dropout(net, 0.5) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam',loss='categorical_crossentropy')

Training

model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=0) model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True, batch_size=128)

Wrong path found when trying to load CIFAR-10 dataset through: import tflearn.datasets.cifar10 as cifar10 X, Y = cifar10.load_data(one_hot=True)

got: Traceback (most recent call last): File "loadcifar1.py", line 3, in <module> X, Y = cifar10.load_data(one_hot=True) File "/usr/local/lib/python2.7/dist-packages/tflearn/datasets/cifar10.py", line 31, in load_data data, labels = load_batch(fpath) File "/usr/local/lib/python2.7/dist-packages/tflearn/datasets/cifar10.py", line 57, in load_batch with open(fpath, 'rb') as f: IOError: [Errno 2] No such file or directory: './cifar-10-batches-py/data_batch_1'

I have dataset which has positive and negative tweets and I want to push these data to IMDb as input. Could you please tell how to do this, sample data which is given below,. Positive. He got appraisal from others. Negative. The movie was not good

I am training a shallow network for each US state. The first analysis works, the second throws the following error, when model.fit is called in this code.

It's a bit confusing that the error message relates to tensorflow instead of tflearn. Maybe tflearn should wrap with tf.Graph().as_default()

File "/DeepOSM/src/single_layer_network.py", line 75, in train_with_data
run_id='mlp')
File "/usr/local/lib/python2.7/dist-packages/tflearn/models/dnn.py", line 153, in fit
self.targets)
File "/usr/local/lib/python2.7/dist-packages/tflearn/utils.py", line 253, in feed_dict_builder
". If you are using notebooks, please " 
Exception: 274 inputs feeded, but expected: 2. If you are using notebooks, please make sure that you didn't run graph construction cell multiple time, or try to enclose your graph within `with tf.Graph().as_default():`

I have following code,

import tensorflow as tf print(tf.version ) tf.executing_eagerly()

===> It prints 2.9.2 and True

!pip install tflearn import tflearn as tfl print(tf.version ) tf.executing_eagerly()

===> It prints 2.9.2 and False

Why? I was expecting it to be True after importing TFLearn.

In keras, there is the option to specify whether the lstm is stateful and the next batch continues the previous sequence or not.

How are the lstm in tflearn handelt?

When I start prediction with data of shape (1, 1, 10) and pass afterwards new data of shape (1, 1, 10), will the lstm take this as (1, 2, 10) data and continue the sequence or does it take it as passing (2, 1, 10) and think it is a new training sequence with timesteps 1.

I'm in a reinforcement setting and can only pass my time series one timestep at a time, so I need to know if I can achieve this using tflearn or if I need to keep track of the state of the lstm. Also, keras requires the initial_state during calling the lstm, not at creation. This also seems to not be the case here.

Tensorflow version: 2.5.1 tflearn version: 0.5.0

Error: ModuleNotFoundError: No module named 'tensorflow.contrib.layers.python.layers.initializers.variance_scaling_initializer'

VarianceScaling now is in tensorflow.keras.initializers.

I wish you will add more examples at tflearn/examples. It would be really cool if you add the Xception model as well. Instead of Keras, tflearn is much more convenient for me, I am not able to write Xception from scratch so i would grateful if you add it👍 💯💯 :)

HELP needed. How can I fix this error?

` def bag_of_words(s, words): bag = [0 for _ in range(len(words))]

s_words = nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words]

for se in s_words:
    for i, w in enumerate(words):
        if w == se:
            bag[i] = 1

return np.array(bag)

def chat(): print("Start talking with the bot! (type quit to stop)") while True: inp = input("You: ") if inp.lower() == "quit": break

    result = model.predict([bag_of_words(inp, words)])[0]
    result_index = np.argmax(result)
    tag = labels[result_index]

    if result[result_index] > 0.7:
        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']
        print(random.choice(responses))

    else:
        print("I didnt get that. Can you explain or try again.")

chat()`

And when I run it I get: Start talking with the bot! (type quit to stop)

You: j Traceback (most recent call last):

File "", line 22, in chat()

File "", line 8, in chat result = model.predict([bag_of_words(inp, words)])

File "C:\Users\monik\anaconda3\lib\site-packages\tflearn\models\dnn.py", line 251, in predict return self.predictor.predict(feed_dict)

File "C:\Users\monik\anaconda3\lib\site-packages\tflearn\helpers\evaluator.py", line 69, in predict return self.session.run(self.tensors[0], feed_dict=feed_dict)

File "C:\Users\monik\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\client\session.py", line 967, in run result = self._run(None, fetches, feed_dict, options_ptr,

File "C:\Users\monik\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\client\session.py", line 1164, in _run raise ValueError(

ValueError: Cannot feed value of shape (61,) for Tensor 'InputData/X:0', which has shape '(?, 61)'

I tried to reshape it, but still it doesn't work