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 27ms/sample - loss: 3.9200 Epoch 2/100 5/5 [==============================] - 0s 169us/sample - loss: 2.3382 Epoch 3/100 5/5 [==============================] - 0s 142us/sample - loss: 1.4161 Epoch 4/100 5/5 [==============================] - 0s 125us/sample - loss: 0.8784 Epoch 5/100 5/5 [==============================] - 0s 121us/sample - loss: 0.5646 Epoch 6/100 5/5 [==============================] - 0s 120us/sample - loss: 0.3814 Epoch 7/100 5/5 [==============================] - 0s 116us/sample - loss: 0.2743 Epoch 8/100 5/5 [==============================] - 0s 123us/sample - loss: 0.2116 Epoch 9/100 5/5 [==============================] - 0s 117us/sample - loss: 0.1747 Epoch 10/100 5/5 [==============================] - 0s 116us/sample - loss: 0.1528 Epoch 11/100 5/5 [==============================] - 0s 117us/sample - loss: 0.1397 Epoch 12/100 5/5 [==============================] - 0s 116us/sample - loss: 0.1317 Epoch 13/100 5/5 [==============================] - 0s 114us/sample - loss: 0.1268 Epoch 14/100 5/5 [==============================] - 0s 162us/sample - loss: 0.1235 Epoch 15/100 5/5 [==============================] - 0s 143us/sample - loss: 0.1213 Epoch 16/100 5/5 [==============================] - 0s 120us/sample - loss: 0.1197 Epoch 17/100 5/5 [==============================] - 0s 167us/sample - loss: 0.1184 Epoch 18/100 5/5 [==============================] - 0s 179us/sample - loss: 0.1173 Epoch 19/100 5/5 [==============================] - 0s 175us/sample - loss: 0.1164 Epoch 20/100 5/5 [==============================] - 0s 177us/sample - loss: 0.1155 Epoch 21/100 5/5 [==============================] - 0s 176us/sample - loss: 0.1146 Epoch 22/100 5/5 [==============================] - 0s 176us/sample - loss: 0.1138 Epoch 23/100 5/5 [==============================] - 0s 174us/sample - loss: 0.1130 Epoch 24/100 5/5 [==============================] - 0s 177us/sample - loss: 0.1123 Epoch 25/100 5/5 [==============================] - 0s 174us/sample - loss: 0.1115 Epoch 26/100 5/5 [==============================] - 0s 183us/sample - loss: 0.1108 Epoch 27/100 5/5 [==============================] - 0s 183us/sample - loss: 0.1100 Epoch 28/100 5/5 [==============================] - 0s 168us/sample - loss: 0.1093 Epoch 29/100 5/5 [==============================] - 0s 168us/sample - loss: 0.1085 Epoch 30/100 5/5 [==============================] - 0s 179us/sample - loss: 0.1078 Epoch 31/100 5/5 [==============================] - 0s 171us/sample - loss: 0.1071 Epoch 32/100 5/5 [==============================] - 0s 172us/sample - loss: 0.1063 Epoch 33/100 5/5 [==============================] - 0s 179us/sample - loss: 0.1056 Epoch 34/100 5/5 [==============================] - 0s 180us/sample - loss: 0.1049 Epoch 35/100 5/5 [==============================] - 0s 184us/sample - loss: 0.1042 Epoch 36/100 5/5 [==============================] - 0s 176us/sample - loss: 0.1035 Epoch 37/100 5/5 [==============================] - 0s 175us/sample - loss: 0.1028 Epoch 38/100 5/5 [==============================] - 0s 181us/sample - loss: 0.1021 Epoch 39/100 5/5 [==============================] - 0s 176us/sample - loss: 0.1014 Epoch 40/100 5/5 [==============================] - 0s 175us/sample - loss: 0.1007 Epoch 41/100 5/5 [==============================] - 0s 175us/sample - loss: 0.1000 Epoch 42/100 5/5 [==============================] - 0s 180us/sample - loss: 0.0994 Epoch 43/100 5/5 [==============================] - 0s 173us/sample - loss: 0.0987 Epoch 44/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0980 Epoch 45/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0974 Epoch 46/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0967 Epoch 47/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0961 Epoch 48/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0954 Epoch 49/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0948 Epoch 50/100 5/5 [==============================] - 0s 182us/sample - loss: 0.0941 Epoch 51/100 5/5 [==============================] - 0s 180us/sample - loss: 0.0935 Epoch 52/100 5/5 [==============================] - 0s 188us/sample - loss: 0.0929 Epoch 53/100 5/5 [==============================] - 0s 187us/sample - loss: 0.0922 Epoch 54/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0916 Epoch 55/100 5/5 [==============================] - 0s 174us/sample - loss: 0.0910 Epoch 56/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0904 Epoch 57/100 5/5 [==============================] - 0s 175us/sample - loss: 0.0898 Epoch 58/100 5/5 [==============================] - 0s 173us/sample - loss: 0.0892 Epoch 59/100 5/5 [==============================] - 0s 180us/sample - loss: 0.0886 Epoch 60/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0880 Epoch 61/100 5/5 [==============================] - 0s 185us/sample - loss: 0.0874 Epoch 62/100 5/5 [==============================] - 0s 181us/sample - loss: 0.0868 Epoch 63/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0862 Epoch 64/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0856 Epoch 65/100 5/5 [==============================] - 0s 188us/sample - loss: 0.0850 Epoch 66/100 5/5 [==============================] - 0s 182us/sample - loss: 0.0845 Epoch 67/100 5/5 [==============================] - 0s 618us/sample - loss: 0.0839 Epoch 68/100 5/5 [==============================] - 0s 191us/sample - loss: 0.0833 Epoch 69/100 5/5 [==============================] - 0s 187us/sample - loss: 0.0828 Epoch 70/100 5/5 [==============================] - 0s 191us/sample - loss: 0.0822 Epoch 71/100 5/5 [==============================] - 0s 197us/sample - loss: 0.0816 Epoch 72/100 5/5 [==============================] - 0s 193us/sample - loss: 0.0811 Epoch 73/100 5/5 [==============================] - 0s 187us/sample - loss: 0.0806 Epoch 74/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0800 Epoch 75/100 5/5 [==============================] - 0s 178us/sample - loss: 0.0795 Epoch 76/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0789 Epoch 77/100 5/5 [==============================] - 0s 206us/sample - loss: 0.0784 Epoch 78/100 5/5 [==============================] - 0s 183us/sample - loss: 0.0779 Epoch 79/100 5/5 [==============================] - 0s 184us/sample - loss: 0.0773 Epoch 80/100 5/5 [==============================] - 0s 184us/sample - loss: 0.0768 Epoch 81/100 5/5 [==============================] - 0s 181us/sample - loss: 0.0763 Epoch 82/100 5/5 [==============================] - 0s 181us/sample - loss: 0.0758 Epoch 83/100 5/5 [==============================] - 0s 182us/sample - loss: 0.0753 Epoch 84/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0748 Epoch 85/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0743 Epoch 86/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0738 Epoch 87/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0733 Epoch 88/100 5/5 [==============================] - 0s 177us/sample - loss: 0.0728 Epoch 89/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0723 Epoch 90/100 5/5 [==============================] - 0s 171us/sample - loss: 0.0718 Epoch 91/100 5/5 [==============================] - 0s 170us/sample - loss: 0.0713 Epoch 92/100 5/5 [==============================] - 0s 184us/sample - loss: 0.0708 Epoch 93/100 5/5 [==============================] - 0s 176us/sample - loss: 0.0703 Epoch 94/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0699 Epoch 95/100 5/5 [==============================] - 0s 172us/sample - loss: 0.0694 Epoch 96/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0689 Epoch 97/100 5/5 [==============================] - 0s 175us/sample - loss: 0.0685 Epoch 98/100 5/5 [==============================] - 0s 179us/sample - loss: 0.0680 Epoch 99/100 5/5 [==============================] - 0s 181us/sample - loss: 0.0675 Epoch 100/100 5/5 [==============================] - 0s 184us/sample - loss: 0.0671 [[22.07085]]
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.