def train(self, training_df, learning_rate=0.001, batch_size=126, model_name="softmax_model"):
column_list = training_df.columns.tolist()
threshold = 5
red_wine_cleaned = training_df.copy()
red_wine_cleaned = _outliers(red_wine_cleaned, threshold, column_list[0:-1])
# Bin the data
bins = [3, 5, 6, 8]
red_wine_cleaned['category'] = pd.cut(red_wine_cleaned.quality, bins, labels=['Bad', 'Average', 'Good'],
include_lowest=True)
# Only include 'Bad' and 'Good' categories
red_wine_newcats = red_wine_cleaned[red_wine_cleaned['category'].isin(['Bad', 'Good'])].copy()
bins = [3, 5, 8]
red_wine_newcats['category'] = pd.cut(red_wine_newcats.quality,
bins, labels=['Bad', 'Good'], include_lowest=True)
y_red_wine = red_wine_newcats[['category']].get_values()
# Removing fixed_acidity and quality
X_red_wine = red_wine_newcats.iloc[:, 1:-2].get_values()
y_red_wine_raveled = y_red_wine.ravel()
y_red_wine_integers = [y.replace('Bad', '1') for y in y_red_wine_raveled]
y_red_wine_integers = [y.replace('Good', '0') for y in y_red_wine_integers]
y_red_wine_integers = [np.int(y) for y in y_red_wine_integers]
y_one_hot = _dense_to_one_hot(y_red_wine_integers, num_classes=2)
X_train, X_test, y_train, y_test = train_test_split(X_red_wine, y_one_hot, test_size=0.2, random_state=42)
# model
with tf.variable_scope("softmax_regression"):
X = tf.placeholder("float", [None, 10])
y, variables = softmax_regression(X)
# train
y_ = tf.placeholder("float", [None, 2])
cost = -tf.reduce_mean(y_ * tf.log(y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
init = tf.initialize_all_variables()
self.sess.run(init)
for i in range(100):
average_cost = 0
number_of_batches = int(len(X_train) / batch_size)
for start, end in zip(range(0, len(X_train), batch_size), range(batch_size, len(X_train), batch_size)):
self.sess.run(optimizer, feed_dict={X: X_train[start:end], y_: y_train[start:end]})
# Compute average loss
average_cost += self.sess.run(cost, feed_dict={X: X_train[start:end],
y_: y_train[start:end]}) / number_of_batches
print(self.sess.run(accuracy, feed_dict={X: X_test, y_: y_test}))
filename = "data/softmax_regression.ckpt"
path = self.save_locally(filename)
self.save_to_s3(path, model_name)
print("Saved:", path)
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