adding trained model

Author: sebastianbrzustowicz

Churn.csv

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+import os
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+import tensorflow as tf
+print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

info.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+df = pd.read_csv("Churn.csv")
+
+X = pd.get_dummies(df.drop(["Churn", "Customer ID"], axis=1))
+y = df["Churn"].apply(lambda x: 1 if x == "Yes" else 0)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# Show X_train data frame
+#print(X_train.head())
+
+# Show y_train data frame
+#print(y_train.head())
+
+# 1. Import dependencies
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score
+
+# 2. Build and compile model
+model = Sequential()
+model.add(Dense(units=32, activation="relu", input_dim=len(X_train.columns)))
+model.add(Dense(units=64, activation="relu"))
+model.add(Dense(units=1, activation="sigmoid"))
+
+model.compile(loss="binary_crossentropy", optimizer="sgd", metrics=["accuracy"])
+
+# 3. Fir, predict and evaluate
+model.fit(X_train, y_train, epochs=100, batch_size=32)
+
+y_hat = model.predict(X_test)
+y_hat = [0 if val <0.5 else 1 for val in y_hat]
+
+print("\nAccuracy: " + accuracy_score(y_test, y_hat))
+
+# 4. Saving and reloading
+model.save("tfmodel")
+del model
+model = load_model("tfmodel")

main.py

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tfmodel/fingerprint.pb

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