import numpy as np
import matplotlib.pyplot as plt

x_train = np.random.randn(1000)
y_train = np.zeros(1000)

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(units=1, activation='sigmoid', input_dim=1))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='mean_squared_error',
              optimizer=SGD(lr=1.0))

model.layers[0].set_weights([np.array([[2.]]),np.array([3.])])
model.layers[1].set_weights([np.array([[4.]]),np.array([5.])])

rec = model.fit(x_train, y_train, epochs=100, batch_size=100)

vep = np.linspace(1.,100.,100)
fig = plt.figure(figsize=(6,6), dpi=80)
plt.plot(vep,rec.history['loss'], lw=3)
plt.show()