tflearn | Deep learning library featuring a higher-level API for TensorFlow | Machine Learning library

from __future__ import print_function

import numpy as np
import tflearn

# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')

# Load CSV file, indicate that the first column represents   

pip uninstall protobuf
pip uninstall tensorflow

# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled, P  

# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v  

'''
Pedagogical example realization of seq2seq recurrent neural networks, using TensorFlow and TFLearn.
More info at https://github.com/ichuang/tflearn_seq2seq
'''

from __future__ import division, print_function

import os
import sys
import tflearn
  

'''
Demonstrate that weights saved with models in one scope, can be loaded 
into models being used in a different scope.

This allows multiple models to be run, and combined models to load
weights from separately trained models.
'''

from __future__   

# -*- coding: utf-8 -*-
"""
Simple Example to train logical operators
"""

from __future__ import absolute_import, division, print_function

import tensorflow.compat.v1 as tf
import tflearn

# Logical NOT operator
X = [[0.], [1.]]
Y = [[1.], [0.]]

#  

with open('intents.json') as file:
    # Here data is used NOT trainer_load
    # If you want to change data to trainer_load change it every where.
    data = json.load(file)

import nltk
from nltk import *
import n

self.trainer.restore(model_file, weights_only, **optargs)
self.session = self.trainer.session

try:
    model.load("model.tflearn")
except:
    model = tflearn.DNN(net)
    model.fit(training, output, n_epoch=1000, 

    def create_model():
        input_data = keras.Input(shape=(number-of-images,shape-of-images))
        ### processing part ###
        model = keras.Model(input_images, your-predictions)
        return model
    model = create_model()

ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(?, 1)'

y = [[int("".join([str(j) for j in i]))] for i in data[["OUT"]].values.tolist()]

y = np.a