In [0]:

    

!pip install -q tf-nightly-gpu-2.0-preview


    

In [2]:

    

import tensorflow as tf
print(tf.__version__)


    

    




2.0.0-dev20190512

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import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow import keras


    

In [4]:

    

!curl -O https://raw.githubusercontent.com/DJCordhose/deep-learning-crash-course-notebooks/master/data/insurance-customers-1500.csv


    

    




  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 26783  100 26783    0     0  26309      0  0:00:01  0:00:01 --:--:-- 26309

In [5]:

    

df = pd.read_csv('./insurance-customers-1500.csv', sep=';')
y = df['group']
df.drop('group', axis='columns', inplace=True)
X = df.as_matrix()


    

    




/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  after removing the cwd from sys.path.

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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)


    

In [7]:

    

from tensorflow.keras.layers import Dense

model = keras.Sequential()

model.add(Dense(units=50, activation='relu', input_dim=3))
model.add(Dense(units=3, activation='softmax'))

model.summary()


    

    




Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 50)                200       
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 153       
=================================================================
Total params: 353
Trainable params: 353
Non-trainable params: 0
_________________________________________________________________

In [8]:

    

BATCH_SIZE=1000
EPOCHS = 2000

model.compile(loss='sparse_categorical_crossentropy',
             optimizer='adam',
             metrics=['accuracy'])

%time history = model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, validation_split=0.2, verbose=0)


    

    




CPU times: user 12.8 s, sys: 1.08 s, total: 13.9 s
Wall time: 11.1 s

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train_loss, train_accuracy = model.evaluate(X_train, y_train, batch_size=BATCH_SIZE)
train_accuracy


    

    




1200/1200 [==============================] - 0s 36us/sample - loss: 0.6709 - accuracy: 0.7375






    
Out[9]:





0.7375

In [10]:

    

# plt.yscale('log')
plt.ylabel("accuracy")
plt.xlabel("epochs")

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])

plt.legend(["Accuracy", "Valdation Accuracy"])


    

    
Out[10]:





<matplotlib.legend.Legend at 0x7f0dfe672cc0>






    

In [11]:

    

model.predict(np.array([[100, 47, 10]]))


    

    
Out[11]:





array([[0.06208737, 0.88149613, 0.05641647]], dtype=float32)

In [12]:

    

test_loss, test_accuracy = model.evaluate(X_test, y_test, batch_size=BATCH_SIZE)
test_accuracy


    

    




300/300 [==============================] - 0s 85us/sample - loss: 0.6550 - accuracy: 0.7500






    
Out[12]:





0.75

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