Created
January 12, 2018 16:37
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import numpy | |
import pandas | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.wrappers.scikit_learn import KerasClassifier | |
from keras.utils import np_utils | |
from sklearn.model_selection import cross_val_score | |
from sklearn.model_selection import KFold | |
from sklearn.preprocessing import LabelEncoder | |
seed = 10 | |
numpy.random.seed(seed) | |
dataframe = pandas.read_csv("iris.csv", header=None) | |
dataset = dataframe.values | |
X = dataset[:,0:4].astype(float) | |
Y = dataset[:,4] | |
encoder = LabelEncoder() | |
encoder.fit(Y) | |
encoded_Y = encoder.transform(Y) | |
dummy_y = np_utils.to_categorical(encoded_Y) | |
model = Sequential() | |
model.add(Dense(4, input_dim=4, activation='relu')) | |
model.add(Dense(3, activation='sigmoid')) | |
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
model.fit(X, dummy_y, epochs=20, batch_size=5) | |
model.save('my_model.h5') |
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