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Linear Regression
Linear Regression
from keras.models import Sequential from keras.layers import Dense import numpy as np import matplotlib.pyplot as plt%matplotlib inlineUsing TensorFlow backend.# Generate dataset trX = np.linspace( -1 , , )trY = * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noise# Linear regression model model = Sequential()model.add(Dense(input_dim=, output_dim=, init= 'uniform' , activation= 'linear' ))model.compile(optimizer= 'sgd' , loss= 'mse' )# Print initial weights weights = model.layers[].get_weights()w_init = weights[][][]b_init = weights[][]print( 'Linear regression model is initialized with weight w: %.2f, b: %.2f' % (w_init, b_init))Linear regression model is initialized with weight w: -0.02, b: 0.00# Train model.fit(trX, trY, nb_epoch=, verbose=)Epoch 1/100101/101 [==============================] - 0s - loss: 1.6034 Epoch 2/100101/101 [==============================] - 0s - loss: 1.5246 Epoch 3/100101/101 [==============================] - 0s - loss: 1.4446 Epoch 4/100101/101 [==============================] - 0s - loss: 1.3690 Epoch 5/100101/101 [==============================] - 0s - loss: 1.2975 Epoch 6/100101/101 [==============================] - 0s - loss: 1.2362 Epoch 7/100101/101 [==============================] - 0s - loss: 1.1691 Epoch 8/100101/101 [==============================] - 0s - loss: 1.1179 Epoch 9/100101/101 [==============================] - 0s - loss: 1.0717 Epoch 10/100101/101 [==============================] - 0s - loss: 1.0224 Epoch 11/100101/101 [==============================] - 0s - loss: 0.9743 Epoch 12/100101/101 [==============================] - 0s - loss: 0.9290 Epoch 13/100101/101 [==============================] - 0s - loss: 0.8886 Epoch 14/100101/101 [==============================] - 0s - loss: 0.8452 Epoch 15/100101/101 [==============================] - 0s - loss: 0.8061 Epoch 16/100101/101 [==============================] - 0s - loss: 0.7721 Epoch 17/100101/101 [==============================] - 0s - loss: 0.7335 Epoch 18/100101/101 [==============================] - 0s - loss: 0.6992 Epoch 19/100101/101 [==============================] - 0s - loss: 0.6663 Epoch 20/100101/101 [==============================] - 0s - loss: 0.6388 Epoch 21/100101/101 [==============================] - 0s - loss: 0.6106 Epoch 22/100101/101 [==============================] - 0s - loss: 0.5842 Epoch 23/100101/101 [==============================] - 0s - loss: 0.5584 Epoch 24/100101/101 [==============================] - 0s - loss: 0.5357 Epoch 25/100101/101 [==============================] - 0s - loss: 0.5126 Epoch 26/100101/101 [==============================] - 0s - loss: 0.4931 Epoch 27/100101/101 [==============================] - 0s - loss: 0.4715 Epoch 28/100101/101 [==============================] - 0s - loss: 0.4544 Epoch 29/100101/101 [==============================] - 0s - loss: 0.4364 Epoch 30/100101/101 [==============================] - 0s - loss: 0.4194 Epoch 31/100101/101 [==============================] - 0s - loss: 0.4032 Epoch 32/100101/101 [==============================] - 0s - loss: 0.3883 Epoch 33/100101/101 [==============================] - 0s - loss: 0.3703 Epoch 34/100101/101 [==============================] - 0s - loss: 0.3565 Epoch 35/100101/101 [==============================] - 0s - loss: 0.3433 Epoch 36/100101/101 [==============================] - 0s - loss: 0.3312 Epoch 37/100101/101 [==============================] - 0s - loss: 0.3179 Epoch 38/100101/101 [==============================] - 0s - loss: 0.3067 Epoch 39/100101/101 [==============================] - 0s - loss: 0.2956 Epoch 40/100101/101 [==============================] - 0s - loss: 0.2856 Epoch 41/100101/101 [==============================] - 0s - loss: 0.2767 Epoch 42/100101/101 [==============================] - 0s - loss: 0.2660 Epoch 43/100101/101 [==============================] - 0s - loss: 0.2579 Epoch 44/100101/101 [==============================] - 0s - loss: 0.2498 Epoch 45/100101/101 [==============================] - 0s - loss: 0.2428 Epoch 46/100101/101 [==============================] - 0s - loss: 0.2355 Epoch 47/100101/101 [==============================] - 0s - loss: 0.2281 Epoch 48/100101/101 [==============================] - 0s - loss: 0.2222 Epoch 49/100101/101 [==============================] - 0s - loss: 0.2158 Epoch 50/100101/101 [==============================] - 0s - loss: 0.2101 Epoch 51/100101/101 [==============================] - 0s - loss: 0.2054 Epoch 52/100101/101 [==============================] - 0s - loss: 0.2013 Epoch 53/100101/101 [==============================] - 0s - loss: 0.1963 Epoch 54/100101/101 [==============================] - 0s - loss: 0.1918 Epoch 55/100101/101 [==============================] - 0s - loss: 0.1870 Epoch 56/100101/101 [==============================] - 0s - loss: 0.1836 Epoch 57/100101/101 [==============================] - 0s - loss: 0.1800 Epoch 58/100101/101 [==============================] - 0s - loss: 0.1760 Epoch 59/100101/101 [==============================] - 0s - loss: 0.1721 Epoch 60/100101/101 [==============================] - 0s - loss: 0.1691 Epoch 61/100101/101 [==============================] - 0s - loss: 0.1664 Epoch 62/100101/101 [==============================] - 0s - loss: 0.1631 Epoch 63/100101/101 [==============================] - 0s - loss: 0.1602 Epoch 64/100101/101 [==============================] - 0s - loss: 0.1570 Epoch 65/100101/101 [==============================] - 0s - loss: 0.1542 Epoch 66/100101/101 [==============================] - 0s - loss: 0.1519 Epoch 67/100101/101 [==============================] - 0s - loss: 0.1496 Epoch 68/100101/101 [==============================] - 0s - loss: 0.1470 Epoch 69/100101/101 [==============================] - 0s - loss: 0.1446 Epoch 70/100101/101 [==============================] - 0s - loss: 0.1430 Epoch 71/100101/101 [==============================] - 0s - loss: 0.1414 Epoch 72/100101/101 [==============================] - 0s - loss: 0.1397 Epoch 73/100101/101 [==============================] - 0s - loss: 0.1381 Epoch 74/100101/101 [==============================] - 0s - loss: 0.1364 Epoch 75/100101/101 [==============================] - 0s - loss: 0.1352 Epoch 76/100101/101 [==============================] - 0s - loss: 0.1338 Epoch 77/100101/101 [==============================] - 0s - loss: 0.1321 Epoch 78/100101/101 [==============================] - 0s - loss: 0.1312 Epoch 79/100101/101 [==============================] - 0s - loss: 0.1299 Epoch 80/100101/101 [==============================] - 0s - loss: 0.1286 Epoch 81/100101/101 [==============================] - 0s - loss: 0.1270 Epoch 82/100101/101 [==============================] - 0s - loss: 0.1260 Epoch 83/100101/101 [==============================] - 0s - loss: 0.1252 Epoch 84/100101/101 [==============================] - 0s - loss: 0.1247 Epoch 85/100101/101 [==============================] - 0s - loss: 0.1239 Epoch 86/100101/101 [==============================] - 0s - loss: 0.1228 Epoch 87/100101/101 [==============================] - 0s - loss: 0.1222 Epoch 88/100101/101 [==============================] - 0s - loss: 0.1214 Epoch 89/100101/101 [==============================] - 0s - loss: 0.1210 Epoch 90/100101/101 [==============================] - 0s - loss: 0.1201 Epoch 91/100101/101 [==============================] - 0s - loss: 0.1194 Epoch 92/100101/101 [==============================] - 0s - loss: 0.1189 Epoch 93/100101/101 [==============================] - 0s - loss: 0.1184 Epoch 94/100101/101 [==============================] - 0s - loss: 0.1176 Epoch 95/100101/101 [==============================] - 0s - loss: 0.1168 Epoch 96/100101/101 [==============================] - 0s - loss: 0.1162 Epoch 97/100101/101 [==============================] - 0s - loss: 0.1157 Epoch 98/100101/101 [==============================] - 0s - loss: 0.1150 Epoch 99/100101/101 [==============================] - 0s - loss: 0.1144 Epoch 100/100101/101 [==============================] - 0s - loss: 0.1140
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