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November 4, 2018 07:37
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BT4221 Assignment 4 Question 1 - Pima Indians Dataset
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#### Create first network with Keras | |
from keras.models import Model | |
from keras.layers import Input, Dense | |
from keras.callbacks import ModelCheckpoint, EarlyStopping | |
import numpy as np | |
batch_size = 10 | |
epochs = 10 | |
validation_split = 0.1 | |
# Load Pima Indians Dataset | |
data = np.loadtxt("pima-indians-diabetes.csv", delimiter=',', skiprows=1) | |
#data = np.loadtxt("data.csv", delimiter=',', skiprows=1) | |
X = data[:,0:8] | |
Y = data[:,8] | |
# Create Model | |
input_layer = Input(shape=(8,)) #input layer | |
layer = Dense(10, activation='tanh')(input_layer) #hidden layer | |
output_layer = Dense(1, activation = 'sigmoid')(layer) #output layer | |
model = Model(inputs=input_layer, outputs= output_layer) | |
# Compile Model | |
model.compile(loss='binary_crossentropy', | |
optimizer='adam', | |
metrics=['accuracy']) | |
# Fit the Model | |
model.fit(x=X, y=Y, | |
batch_size=batch_size, | |
epochs=epochs, | |
validation_split=0.33) | |
# Evaluate the model | |
scores = model.evaluate(X, Y) | |
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) | |
#for fun | |
from keras.utils import plot_model | |
plot_model(model, to_file='model.png') |
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