Neural network:feed forward and trained_model saved for future use of…

… predictions

python/feedforward_nn.py

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+# Train model and make predictions
+import numpy as np
+import pandas
+import h5py
+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 train_test_split
+from sklearn.preprocessing import LabelEncoder
+# fix random seed for reproducibility
+seed = 7
+np.random.seed(seed)
+# load dataset
+dataframe = pandas.read_csv("../data/scaled_data/scaled_pca.csv")
+dataframe = dataframe.sample(frac=1).reset_index(drop=True)
+dataset = dataframe.values
+X = dataset[:,:-1].astype(float)
+Y = dataset[:,-1]
+# encode class values as integers
+encoder = LabelEncoder()
+encoder.fit(Y)
+encoded_Y = encoder.transform(Y)
+# convert integers to dummy variables (i.e. one hot encoded)
+dummy_y = np_utils.to_categorical(encoded_Y)
+
+# create model
+model = Sequential()
+model.add(Dense(12, input_dim=75, kernel_initializer='uniform', activation='relu'))
+model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
+model.add(Dense(3, kernel_initializer='normal', activation='sigmoid'))
+# Compile model
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+# Fit the model
+model.fit(X[:], dummy_y, nb_epoch=1000, batch_size=150,  verbose=1)
+
+# calculate predictions
+predictions = model.predict(X)
+print predictions
+predictions = np.argmax(predictions,axis=1)
+classes = np.unique(Y)
+p = classes[predictions]
+# print the accuracy
+print sum(Y == p)/float(len(Y))
+
+import pickle
+with open('../trained_models/ff_nn_1.pkl','wb') as f:
+	pickle.dump(model)