ibrahim gad ibrahim85

## GradientDescent.ipynb

      
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                ibrahim85
                / GradientDescent.ipynb
            
            
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              August 24, 2019 18:37
                — forked from sagarmainkar/GradientDescent.ipynb
            
          
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## classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## keras.py
""" From:  http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
from keras.models import Sequential
from keras.layers.core import TimeDistributedDense, Activation, Dropout
from keras.layers.recurrent import GRU
import numpy as np

def _load_data(data, steps = 40):
    docX, docY = [], []
    for i in range(0, data.shape[0]/steps-1):
        docX.append(data[i*steps:(i+1)*steps,:])

## keras_prediction.py
import pandas as pd
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
pdata = pd.DataFrame({"a":flow, "b":flow})
pdata.b = pdata.b.shift(9)
data = pdata.iloc[10:] * random()  # some noise

import numpy as np

## anaconda-tensorflow-keras.txt
Install TensorFlow (CPU), Keras, and some other tools to a new anaconda environment.

Open Anaconda Prompt

conda create --name tensorflow35 python=3.5
conda activate tensorflow35
conda install jupyter
conda install scipy
conda install spyder
pip install tensorflow
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	""" From: http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
	from keras.models import Sequential
	from keras.layers.core import TimeDistributedDense, Activation, Dropout
	from keras.layers.recurrent import GRU
	import numpy as np

	def _load_data(data, steps = 40):
	docX, docY = [], []
	for i in range(0, data.shape[0]/steps-1):
	docX.append(data[isteps:(i+1)steps,:])
	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np
	Install TensorFlow (CPU), Keras, and some other tools to a new anaconda environment.

	Open Anaconda Prompt

	conda create --name tensorflow35 python=3.5
	conda activate tensorflow35
	conda install jupyter
	conda install scipy
	conda install spyder
	pip install tensorflow