Tensorflow 2

tf2.ipynb

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    "def custom_model(X, y):\n",
    "    layers = skflow.ops.dnn(X, [20, 40, 20], tf.tanh)\n",
    "    return skflow.models.logistic_regression(layers, y)"
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     "text": [
      "Step #100, epoch #25, avg. train loss: 0.61585\n",
      "Step #200, epoch #50, avg. train loss: 0.59296\n",
      "Step #300, epoch #75, avg. train loss: 0.56212\n",
      "Step #400, epoch #100, avg. train loss: 0.53000\n",
      "Step #500, epoch #125, avg. train loss: 0.51094\n",
      "Step #600, epoch #150, avg. train loss: 0.48331\n",
      "Step #700, epoch #175, avg. train loss: 0.47346\n",
      "Step #800, epoch #200, avg. train loss: 0.46050\n",
      "Step #900, epoch #225, avg. train loss: 0.45013\n",
      "Step #1000, epoch #250, avg. train loss: 0.44446\n"
     ]
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       "0.80382775119617222"
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   "source": [
    "tf_clf_c = skflow.TensorFlowEstimator(model_fn=custom_model, n_classes=2, batch_size=256, steps=1000, learning_rate=0.05)\n",
    "tf_clf_c.fit(X_train, y_train)\n",
    "metrics.accuracy_score(y_test, tf_clf_c.predict(X_test))"
   ]
  }
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