import numpy as np import tensorflow as tf # os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' # import keras # Generate some example data (x values and corresponding y values) x_train = np.array([1, 2, 3, 4, 5], dtype=float) y_train = np.array([3, 5, 7, 9, 11], dtype=float) # y = 2x + 1 # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(units=1, input_shape=[1]) ]) # Compile the model model.compile(optimizer='sgd', loss='mean_squared_error') # Train the model model.fit(x_train, y_train, epochs=100) # Predict print(model.predict([10])) # Should output something close to 21 (since y = 2*10 + 1)
Standard input is empty
Epoch 1/100 5/5 [==============================] - 0s 18ms/sample - loss: 11.7218 Epoch 2/100 5/5 [==============================] - 0s 217us/sample - loss: 6.8713 Epoch 3/100 5/5 [==============================] - 0s 206us/sample - loss: 4.0444 Epoch 4/100 5/5 [==============================] - 0s 200us/sample - loss: 2.3967 Epoch 5/100 5/5 [==============================] - 0s 215us/sample - loss: 1.4363 Epoch 6/100 5/5 [==============================] - 0s 225us/sample - loss: 0.8763 Epoch 7/100 5/5 [==============================] - 0s 233us/sample - loss: 0.5497 Epoch 8/100 5/5 [==============================] - 0s 213us/sample - loss: 0.3592 Epoch 9/100 5/5 [==============================] - 0s 211us/sample - loss: 0.2478 Epoch 10/100 5/5 [==============================] - 0s 227us/sample - loss: 0.1827 Epoch 11/100 5/5 [==============================] - 0s 214us/sample - loss: 0.1445 Epoch 12/100 5/5 [==============================] - 0s 218us/sample - loss: 0.1220 Epoch 13/100 5/5 [==============================] - 0s 196us/sample - loss: 0.1086 Epoch 14/100 5/5 [==============================] - 0s 182us/sample - loss: 0.1005 Epoch 15/100 5/5 [==============================] - 0s 173us/sample - loss: 0.0956 Epoch 16/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0925 Epoch 17/100 5/5 [==============================] - 0s 184us/sample - loss: 0.0904 Epoch 18/100 5/5 [==============================] - 0s 189us/sample - loss: 0.0889 Epoch 19/100 5/5 [==============================] - 0s 185us/sample - loss: 0.0878 Epoch 20/100 5/5 [==============================] - 0s 194us/sample - loss: 0.0870 Epoch 21/100 5/5 [==============================] - 0s 188us/sample - loss: 0.0862 Epoch 22/100 5/5 [==============================] - 0s 185us/sample - loss: 0.0855 Epoch 23/100 5/5 [==============================] - 0s 191us/sample - loss: 0.0849 Epoch 24/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0843 Epoch 25/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0837 Epoch 26/100 5/5 [==============================] - 0s 178us/sample - loss: 0.0831 Epoch 27/100 5/5 [==============================] - 0s 196us/sample - loss: 0.0826 Epoch 28/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0820 Epoch 29/100 5/5 [==============================] - 0s 178us/sample - loss: 0.0814 Epoch 30/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0809 Epoch 31/100 5/5 [==============================] - 0s 188us/sample - loss: 0.0803 Epoch 32/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0798 Epoch 33/100 5/5 [==============================] - 0s 178us/sample - loss: 0.0793 Epoch 34/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0787 Epoch 35/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0782 Epoch 36/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0777 Epoch 37/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0771 Epoch 38/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0766 Epoch 39/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0761 Epoch 40/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0756 Epoch 41/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0751 Epoch 42/100 5/5 [==============================] - 0s 168us/sample - loss: 0.0746 Epoch 43/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0741 Epoch 44/100 5/5 [==============================] - 0s 167us/sample - loss: 0.0736 Epoch 45/100 5/5 [==============================] - 0s 168us/sample - loss: 0.0731 Epoch 46/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0726 Epoch 47/100 5/5 [==============================] - 0s 182us/sample - loss: 0.0721 Epoch 48/100 5/5 [==============================] - 0s 171us/sample - loss: 0.0716 Epoch 49/100 5/5 [==============================] - 0s 169us/sample - loss: 0.0711 Epoch 50/100 5/5 [==============================] - 0s 171us/sample - loss: 0.0706 Epoch 51/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0702 Epoch 52/100 5/5 [==============================] - 0s 178us/sample - loss: 0.0697 Epoch 53/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0692 Epoch 54/100 5/5 [==============================] - 0s 169us/sample - loss: 0.0688 Epoch 55/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0683 Epoch 56/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0678 Epoch 57/100 5/5 [==============================] - 0s 168us/sample - loss: 0.0674 Epoch 58/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0669 Epoch 59/100 5/5 [==============================] - 0s 173us/sample - loss: 0.0665 Epoch 60/100 5/5 [==============================] - 0s 169us/sample - loss: 0.0660 Epoch 61/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0656 Epoch 62/100 5/5 [==============================] - 0s 166us/sample - loss: 0.0651 Epoch 63/100 5/5 [==============================] - 0s 171us/sample - loss: 0.0647 Epoch 64/100 5/5 [==============================] - 0s 167us/sample - loss: 0.0642 Epoch 65/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0638 Epoch 66/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0634 Epoch 67/100 5/5 [==============================] - 0s 542us/sample - loss: 0.0630 Epoch 68/100 5/5 [==============================] - 0s 183us/sample - loss: 0.0625 Epoch 69/100 5/5 [==============================] - 0s 188us/sample - loss: 0.0621 Epoch 70/100 5/5 [==============================] - 0s 180us/sample - loss: 0.0617 Epoch 71/100 5/5 [==============================] - 0s 171us/sample - loss: 0.0613 Epoch 72/100 5/5 [==============================] - 0s 144us/sample - loss: 0.0609 Epoch 73/100 5/5 [==============================] - 0s 120us/sample - loss: 0.0604 Epoch 74/100 5/5 [==============================] - 0s 111us/sample - loss: 0.0600 Epoch 75/100 5/5 [==============================] - 0s 114us/sample - loss: 0.0596 Epoch 76/100 5/5 [==============================] - 0s 113us/sample - loss: 0.0592 Epoch 77/100 5/5 [==============================] - 0s 127us/sample - loss: 0.0588 Epoch 78/100 5/5 [==============================] - 0s 115us/sample - loss: 0.0584 Epoch 79/100 5/5 [==============================] - 0s 113us/sample - loss: 0.0580 Epoch 80/100 5/5 [==============================] - 0s 115us/sample - loss: 0.0576 Epoch 81/100 5/5 [==============================] - 0s 115us/sample - loss: 0.0573 Epoch 82/100 5/5 [==============================] - 0s 112us/sample - loss: 0.0569 Epoch 83/100 5/5 [==============================] - 0s 114us/sample - loss: 0.0565 Epoch 84/100 5/5 [==============================] - 0s 112us/sample - loss: 0.0561 Epoch 85/100 5/5 [==============================] - 0s 112us/sample - loss: 0.0557 Epoch 86/100 5/5 [==============================] - 0s 114us/sample - loss: 0.0554 Epoch 87/100 5/5 [==============================] - 0s 115us/sample - loss: 0.0550 Epoch 88/100 5/5 [==============================] - 0s 110us/sample - loss: 0.0546 Epoch 89/100 5/5 [==============================] - 0s 112us/sample - loss: 0.0542 Epoch 90/100 5/5 [==============================] - 0s 109us/sample - loss: 0.0539 Epoch 91/100 5/5 [==============================] - 0s 111us/sample - loss: 0.0535 Epoch 92/100 5/5 [==============================] - 0s 110us/sample - loss: 0.0531 Epoch 93/100 5/5 [==============================] - 0s 108us/sample - loss: 0.0528 Epoch 94/100 5/5 [==============================] - 0s 113us/sample - loss: 0.0524 Epoch 95/100 5/5 [==============================] - 0s 109us/sample - loss: 0.0521 Epoch 96/100 5/5 [==============================] - 0s 111us/sample - loss: 0.0517 Epoch 97/100 5/5 [==============================] - 0s 116us/sample - loss: 0.0514 Epoch 98/100 5/5 [==============================] - 0s 113us/sample - loss: 0.0510 Epoch 99/100 5/5 [==============================] - 0s 117us/sample - loss: 0.0507 Epoch 100/100 5/5 [==============================] - 0s 115us/sample - loss: 0.0503 [[21.927652]]
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead.