In [1]:

    

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

import keras
from keras.models import Sequential
from keras.layers import Dense

from keras.utils import to_categorical


    

In [45]:

    

model = Sequential()
model.add(Dense(20, input_shape=(None,4)))
model.add(Dense(10))
model.add(Dense(5))
model.add(Dense(3))


    

In [46]:

    

model.compile('adam', loss='mse')


    

In [5]:

    

from sklearn import datasets


    

In [26]:

    

x, y = datasets.load_iris(return_X_y=True)


    

In [27]:

    

X = x.reshape(1, -1, 4)


    

In [31]:

    

Y = to_categorical(y).reshape(1, -1, 3)


    

In [42]:

    

splt = int(0.7*len(x))


    

In [43]:

    

x_train, x_test = X[:, :splt], X[:, splt:]
y_train, y_test = Y[:, :splt], Y[:, splt:]


    

In [44]:

    

x_train.shape


    

    
Out[44]:





(1, 105, 4)

In [58]:

    

model.fit(x_train, y_train, shuffle=True, epochs=1000, verbose=0)


    

    
Out[58]:





<keras.callbacks.History at 0x7f86d4ce24e0>

In [59]:

    

loss = model.evaluate(x_test, y_test)


    

    




1/1 [==============================] - 0s 1ms/step

In [60]:

    

loss


    

    
Out[60]:





0.6141459941864014