import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import *
import tensorflow as tf
C:\Users\rstancut\AppData\Local\Continuum\anaconda2\envs\keras\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend.
training_data_df = pd.read_csv("Exercise Files/07/sales_data_training_scaled.csv")
X = training_data_df.drop('total_earnings', axis=1).values
Y = training_data_df[['total_earnings']].values
# Define the model
model = Sequential()
model.add(Dense(50, input_dim=9, activation='relu', name='layer_1'))
model.add(Dense(100, activation='relu', name='layer_2'))
model.add(Dense(50, activation='relu', name='layer_3'))
model.add(Dense(1, activation='linear', name='output_layer'))
model.compile(loss='mean_squared_error', optimizer='adam')
# Create a TensorBoard logger
logger = keras.callbacks.TensorBoard(
log_dir='Exercise Files/07/logs',
# histogram_freq=5,
write_graph=True
)
# Train the model
model.fit(
X,
Y,
epochs=50,
shuffle=True,
verbose=2,
callbacks=[logger]
)
Epoch 1/50 - 0s - loss: 0.0227 Epoch 2/50 - 0s - loss: 0.0052 Epoch 3/50 - 0s - loss: 0.0017 Epoch 4/50 - 0s - loss: 5.5748e-04 Epoch 5/50 - 0s - loss: 2.5953e-04 Epoch 6/50 - 0s - loss: 1.7975e-04 Epoch 7/50 - 0s - loss: 1.4039e-04 Epoch 8/50 - 0s - loss: 1.0314e-04 Epoch 9/50 - 0s - loss: 9.1074e-05 Epoch 10/50 - 0s - loss: 1.0132e-04 Epoch 11/50 - 0s - loss: 8.4940e-05 Epoch 12/50 - 0s - loss: 6.2468e-05 Epoch 13/50 - 0s - loss: 5.3496e-05 Epoch 14/50 - 0s - loss: 5.0403e-05 Epoch 15/50 - 0s - loss: 5.9816e-05 Epoch 16/50 - 0s - loss: 4.4133e-05 Epoch 17/50 - 0s - loss: 3.5091e-05 Epoch 18/50 - 0s - loss: 3.6853e-05 Epoch 19/50 - 0s - loss: 3.4347e-05 Epoch 20/50 - 0s - loss: 3.5045e-05 Epoch 21/50 - 0s - loss: 3.4173e-05 Epoch 22/50 - 0s - loss: 3.8343e-05 Epoch 23/50 - 0s - loss: 3.8071e-05 Epoch 24/50 - 0s - loss: 3.8656e-05 Epoch 25/50 - 0s - loss: 3.2387e-05 Epoch 26/50 - 0s - loss: 2.7059e-05 Epoch 27/50 - 0s - loss: 2.4506e-05 Epoch 28/50 - 0s - loss: 2.8591e-05 Epoch 29/50 - 0s - loss: 2.9481e-05 Epoch 30/50 - 0s - loss: 2.7920e-05 Epoch 31/50 - 0s - loss: 2.7750e-05 Epoch 32/50 - 0s - loss: 2.5467e-05 Epoch 33/50 - 0s - loss: 2.3225e-05 Epoch 34/50 - 0s - loss: 4.4590e-05 Epoch 35/50 - 0s - loss: 3.8818e-05 Epoch 36/50 - 0s - loss: 4.8698e-05 Epoch 37/50 - 0s - loss: 3.6104e-05 Epoch 38/50 - 0s - loss: 2.4204e-05 Epoch 39/50 - 0s - loss: 2.9451e-05 Epoch 40/50 - 0s - loss: 3.7958e-05 Epoch 41/50 - 0s - loss: 3.4461e-05 Epoch 42/50 - 0s - loss: 2.6710e-05 Epoch 43/50 - 0s - loss: 2.0069e-05 Epoch 44/50 - 0s - loss: 2.2829e-05 Epoch 45/50 - 0s - loss: 2.7821e-05 Epoch 46/50 - 0s - loss: 3.6467e-05 Epoch 47/50 - 0s - loss: 3.3873e-05 Epoch 48/50 - 0s - loss: 2.2890e-05 Epoch 49/50 - 0s - loss: 2.1494e-05 Epoch 50/50 - 0s - loss: 2.1028e-05
<keras.callbacks.History at 0x2ceaeabb7f0>
# Load the separate test data set
test_data_df = pd.read_csv("Exercise Files/07/sales_data_test_scaled.csv")
X_test = test_data_df.drop('total_earnings', axis=1).values
Y_test = test_data_df[['total_earnings']].values
test_error_rate = model.evaluate(X_test, Y_test, verbose=0)
print("The mean squared error (MSE) for the test data set is: {}".format(test_error_rate))
The mean squared error (MSE) for the test data set is: 7.801730826031416e-05
model_builder = tf.saved_model.builder.SavedModelBuilder("Exercise Files/07/exported_model")
inputs = {
'input': tf.saved_model.utils.build_tensor_info(model.input)
}
outputs = {
'earnings': tf.saved_model.utils.build_tensor_info(model.output)
}
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
model_builder.add_meta_graph_and_variables(
K.get_session(),
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def
}
)
model_builder.save()
INFO:tensorflow:No assets to save. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: b'Exercise Files/07/exported_model\\saved_model.pb'
b'Exercise Files/07/exported_model\\saved_model.pb'