Example Keras

from keras.models import Sequential
from keras.layers import Dense
import numpy
import sklearn.datasets
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
numpy.random.seed(7)

# -----------------------------------------------------------------------------
# generate sinus.
# -----------------------------------------------------------------------------

T = 1000
X = numpy.array(range(T))
Y = numpy.sin(3.5 * numpy.pi * X / T) 

# -----------------------------------------------------------------------------
# Draw training data
# -----------------------------------------------------------------------------

plt.scatter(X, Y, s = 10)
plt.show()

# -----------------------------------------------------------------------------
# Build Keras model (my keras uses TensorFlow backend)
# -----------------------------------------------------------------------------

input_dim = 1

model = Sequential()
model.add(Dense(10, input_dim = input_dim, activation='tanh'))
model.add(Dense(90, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(10, activation='tanh'))
model.add(Dense(1, activation='tanh'))


# -----------------------------------------------------------------------------
# Comile and fit
# -----------------------------------------------------------------------------

model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=50, batch_size=10)

# -----------------------------------------------------------------------------
# Evalute
# -----------------------------------------------------------------------------


scores = model.evaluate(X, Y)
print("-" * 100)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[0]*100))


# -----------------------------------------------------------------------------
# Compare prediction vs groundtruth
# -----------------------------------------------------------------------------

pred =  model.predict(X)
x_plot = X
plt.scatter(x_plot, pred, s = 1, c =  'r')
plt.scatter(x_plot, Y, s = 1, c = 'b')
plt.show()