dock

app.py

@@ -0,0 +1,15 @@
+from flask import Flask
+
+app = Flask(__name__)
+
+@app.route("/demo/")
+def hello_world():
+    import subprocess
+    subprocess.check_output("python3 demo.py", shell=1)
+    return "<p>Hello, World!</p>"
+
+@app.route("/")
+def hello_world1():
+    return "<p>OK!</p>"
+if __name__ == '__main__':
+   app.run(debug=True, port=8080, host="0.0.0.0")

demo.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from fromscratchtoml.neural_network.models import Sequential
+from fromscratchtoml.neural_network.optimizers import StochasticGradientDescent
+from fromscratchtoml.neural_network.layers import Dense, Activation
+
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from fromscratchtoml.toolbox.random import Distribution
+from fromscratchtoml.toolbox.preprocess import to_onehot
+import os
+os.environ['OPENBLAS_NUM_THREADS'] = '1'
+os.environ['MKL_NUM_THREADS'] = '1'
+from sklearn import datasets
+from sklearn.utils import shuffle
+
+from fromscratchtoml.toolbox import binary_visualize
+
+
+
+# ## Radial
+
+# In[2]:
+
+
+X11 = Distribution.radial_binary(pts=300,
+               mean=[0, 0],
+               st=1,
+               ed=2, seed=20)
+X22 = Distribution.radial_binary(pts=300,
+               mean=[0, 0],
+               st=4,
+               ed=5, seed=20)
+
+Y11 = np.ones(X11.shape[0])
+Y22 = np.zeros(X11.shape[0])
+
+X5 = np.vstack((X11, X22))
+y5 = np.hstack((Y11, Y22))
+
+
+# In[3]:
+
+
+y5 = to_onehot(y5)
+
+
+# In[4]:
+
+
+X_train5, X_test5, y_train5, y_test5 = train_test_split(X5, y5, test_size=50, random_state=42)
+
+
+# In[5]:
+
+
+y_train5.shape
+
+
+# In[6]:
+
+
+model5 = Sequential(verbose=1, vis_each_epoch=True)
+model5.add(Dense(10, input_dim=2, seed=1))
+model5.add(Activation('sigmoid'))
+model5.add(Dense(2, seed=2))
+model5.add(Activation('sigmoid'))
+model5.add(Dense(2, seed=3))
+# model5.add(Activation('sigmoid'))
+sgd = StochasticGradientDescent(learning_rate=0.005)
+model5.compile(optimizer=sgd, loss="mean_squared_error")
+
+
+# In[7]:
+
+model5.fit(X_train5, y_train5, batch_size=16, epochs=40)
+
+
+# In[8]:
+
+
+binary_visualize(X_test5, clf=model5, draw_contour=True)

docker/Dockerfile

@@ -0,0 +1,32 @@
+FROM ubuntu:18.04
+
+MAINTAINER Mohit Rathore mrmohitrathoremr@gmail.com
+
+ENV REPOSITORY https://github.com/jellAIfish/fromscratchtoml.git
+ENV BRANCH demo
+
+ENV LC_ALL C.UTF-8
+ENV LANG C.UTF-8
+RUN apt-get update
+RUN apt-get install -y python3-pip git
+
+# Upgrade pip
+RUN pip3 install --upgrade pip
+
+RUN git clone $REPOSITORY
+WORKDIR /fromscratchtoml
+RUN git checkout $BRANCH
+RUN pip3 install -r requirements.txt
+RUN pip3 install jupyter
+RUN pip3 install scikit-learn==0.20.1
+RUN python3 setup.py install
+
+# Fix ipython kernel version
+RUN ipython kernel install
+
+# Add running permission to startup script
+RUN chmod +x /fromscratchtoml/docker/start_jupyter_notebook.sh
+
+# Define the starting command for this container and expose its running port
+CMD sh -c 'flask run -h 0.0.0.0 -p 8080'
+EXPOSE 8080

docker/start_jupyter_notebook.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+PORT=$1
+NOTEBOOK_DIR=/fromscratchtoml/docs/notebooks
+
+jupyter notebook --no-browser --ip=* --port=${PORT} --allow-root --notebook-dir=${NOTEBOOK_DIR} --NotebookApp.token=\"\"

requirements.txt

@@ -1,3 +1,3 @@
 numpy >= 1.14.2
-cvxopt >= 1.2.0
-matplotlib >= 2.1.0
+matplotlib == 2.1.0
+Flask==2.0.3