Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- def df_to_dataset(dataframe, labels, shuffle=True, batch_size=32):
- ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
- if shuffle:
- ds = ds.shuffle(buffer_size=len(dataframe))
- ds = ds.batch(batch_size)
- return ds
- feature_columns = []
- # numeric cols
- for header in list(X_train):
- feature_columns.append(feature_column.numeric_column(header))
- feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
- batch_size = 32
- train_ds = df_to_dataset(X_train, y_train, batch_size=batch_size)
- val_ds = df_to_dataset(X_val, y_val, shuffle=False, batch_size=batch_size)
- test_ds = df_to_dataset(X_test,y_test, shuffle=False,
- batch_size=batch_size)
- model = tf.keras.Sequential([
- feature_layer,
- layers.Dense(128, activation='relu'),
- layers.Dense(128, activation='relu'),
- layers.Dense(1, activation='sigmoid')
- ])
- model.compile(optimizer='adam',
- loss='binary_crossentropy',
- metrics=['accuracy'],)
- #run_eagerly=True)
- model.fit(train_ds,
- validation_data=val_ds,
- epochs=5)
- loss, accuracy = model.evaluate(test_ds)
- print("Accuracy", accuracy)
- W0625 16:28:50.013361 140172694484864 deprecation.py:323] From
- /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180:
- add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops)
- is deprecated and will be removed in a future version.
- Instructions for updating:
- Use tf.where in 2.0, which has the same broadcast rule as np.where
- Epoch 1/5
- 2437/2437 [==============================] - 12s 5ms/step - loss:
- -368933040.3744 - acc: 0.0000e+00 - val_loss: -1374389959.0878 - val_acc:
- 0.0000e+00
- Epoch 2/5
- 2437/2437 [==============================] - 11s 4ms/step - loss:
- -4239125012.7993 - acc: 0.0000e+00 - val_loss: -8055676778.8942 - val_acc:
- 0.0000e+00
- Epoch 3/5
- 2437/2437 [==============================] - 11s 4ms/step - loss:
- -14449097654.0468 - acc: 0.0000e+00 - val_loss: -21844830544.8532 - val_acc:
- 0.0000e+00
- Epoch 4/5
- 2437/2437 [==============================] - 11s 4ms/step - loss:
- -32560744568.1740 - acc: 0.0000e+00 - val_loss: -44181551604.6596 - val_acc:
- 0.0000e+00
- Epoch 5/5
- 2437/2437 [==============================] - 11s 4ms/step - loss:
- -60235093753.8022 - acc: 0.0000e+00 - val_loss: -76823729189.8015 - val_acc:
- 0.0000e+00
- 1219/1219 [==============================] - 3s 2ms/step - loss:
- -78553874677.2896 - acc: 0.0000e+00
- Accuracy 0.0
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement