Coding a Tensor Flow

General Steps

1. Load the neural network packages
2. Read in the data
3. Divide the data into a training set and a test set.
4. Preprocess the data
5. Design the Network Model
6. Train the model
7. Apply the model to the test data
8. Display the results

1. Load Keras Packages

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical

2. Read in the Data

import numpy as np
data_file = 'Data/cancer_data.csv'
target_file = 'Data/cancer_target.csv'
cancer_data=np.loadtxt(data_file,dtype=float, delimiter=',')
cancer_target=np.loadtxt(target_file, dtype=float, delimiter=',')

3. Split the Data

from sklearn import model_selection
test_size = 0.30
seed = 7
train_data, test_data, train_target, test_target = model_selection.train_test_split(cancer_data,cancer_target, test_size=test_size, random_state=seed)

4. Pre-process the Data

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Fit only to the training data
scaler.fit(train_data)
# Now apply the transformations to the data:
x_train = scaler.transform(train_data)
x_test = scaler.transform(test_data)
# Convert the classes to 'one-hot' vector
y_train = to_categorical(train_target, num_classes=2)
y_test = to_categorical(test_target, num_classes=2)

5. Define the Model

def define_model():
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.optimizers import SGD

  model = keras.Sequential([ 
    layers.Dense(30, activation="relu"),
    layers.Dropout(0.5),
    layers.Dense(60, activation="relu"),
    layers.Dropout(0.5),
    layers.Dense(2, activation="softmax")
  ])

  model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])

  return(model)

model = define_model()

7.Fit the Model

b_size = int(.8*x_train.shape[0])
num_epochs = 20
model.fit(x_train, y_train, epochs=num_epochs, batch_size=b_size)

8.Apply the Model to Test Data

predictions = np.argmax(model.predict(x_test), axis=-1)

9.Evaluate the Results

score = model.evaluate(x_test, y_test, batch_size=b_size)
print('\nAccuracy:  %.3f' % score[1])
from sklearn.metrics import confusion_matrix
print(confusion_matrix(test_target, predictions))

Activity: TensorFlow Program

Make sure that you can run the TensorFlow code: 04_TensorFlow.ipynb