Hi All,
I am newbie to scikit-learn. Is it possible to use scikit-learn instead of
tensorflow and keras in the attached script?
Best regards,
Nils
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import random
import math
import numpy as np
np.random.seed(1)
#
# ModuleNotFoundError: No module named 'tensorflow'
#
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
def Amplitude(omega, zeta):
"""Analytic amplitude calculation"""
A = 1/math.sqrt((1-omega**2)**2+(2*zeta*omega)**2)
return A
zeta_0 = 0.1 # Damping ratio
w_min = 0.0 # Start frequency
w_max = 10.0 # End frequency
N_omega = 300 # Number of points per interval
w = np.linspace(w_min, w_max, N_omega).reshape(-1, 1)
Amplitude = np.vectorize(Amplitude)
a = Amplitude(w, zeta_0)
rnd_indices = np.random.rand(len(w)) < 0.80
x_train = w[rnd_indices]
y_train = a[rnd_indices]
x_test = w[~rnd_indices]
y_test = a[~rnd_indices]
print (x_train)
print (x_test)
input('Press enter to continue')
# Create a model
def baseline_model():
height = 100
model = Sequential()
model.add(Dense(height, input_dim=1, activation='tanh',
kernel_initializer='uniform'))
model.add(Dense(height, input_dim=height, activation='tanh',
kernel_initializer='uniform'))
model.add(Dense(height, input_dim=height, activation='tanh',
kernel_initializer='uniform'))
model.add(Dense(1, input_dim=height, activation='linear',
kernel_initializer='uniform'))
sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)
model.compile(loss='mse', optimizer=sgd)
return model
# Training the model
model = baseline_model()
model.fit(x_train, y_train, epochs=1000, verbose = 0)
plt.figure(figsize=(16,8))
plt.rcParams["font.family"] = "arial"
plt.rcParams["font.size"] = "18"
plt.semilogy(x_test, model.predict(x_test), 'og')
plt.semilogy(x_train, model.predict(x_train), 'r')
plt.semilogy(w, a, 'b')
plt.xlabel('Driving Angular Frequency [Hz]')
plt.ylabel('Amplitude [m]')
plt.title('Oscillator Amplitude vs Driving Angular Frequency')
plt.legend(['TensorFlow Test', 'TensorFlow Training', 'Analytic Solution'])
plt.show()
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