[scikit-learn] tensorflow and scikit-learn

[Nils Wagner]

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
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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()