iris.py

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.cross_validation import cross_val_score, KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("iris.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]
print(X)
print(Y)

#encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)


# convert integers to dummy variables (hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)
print(dummy_y)

# define baseline model
#def baseline_model():
# create model
model = Sequential()
model.add(Dense(4, input_dim=4, activation='relu'))
model.add(Dense(3,activation='sigmoid'))

##model.add(Dense(3,activation='sigmoid',output_dim=3))
##model.add(Dense(4, input_dim=4, init='normal', activation='relu'))
##model.add(Dense(3, init='normal', activation='sigmoid'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

#    return model
#model.fit
model.fit(X, dummy_y, nb_epoch=200, batch_size=5)
#estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=200, batch_size=5, verbose=0)
#kfold = KFold(n=len(X), n_folds=10, shuffle=True, random_state=seed)
#results = cross_val_score(estimator, X, dummy_y, cv=kfold)

#print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))



model.save_weights('iris_model_save')
#model.save_model('sm2') ## does not work
json_string = model.to_json()
text_file = open("iris_model_json", "w")
text_file.write(json_string)
text_file.close()