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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,146 @@ import tensorflow as tf import pandas as pd from sklearn.cross_validation import train_test_split FILE_PATH = '~/Desktop/bank-add/bank_equalized.csv' # Path to .csv dataset raw_data = pd.read_csv(FILE_PATH) # Open raw .csv print("Raw data loaded successfully...\n") #------------------------------------------------------------------------------ # Variables Y_LABEL = 'y' # Name of the variable to be predicted KEYS = [i for i in raw_data.keys().tolist() if i != Y_LABEL] # Name of predictors N_INSTANCES = raw_data.shape[0] # Number of instances N_INPUT = raw_data.shape[1] - 1 # Input size N_CLASSES = raw_data[Y_LABEL].unique().shape[0] # Number of classes (output size) TEST_SIZE = 0.1 # Test set size (% of dataset) TRAIN_SIZE = int(N_INSTANCES * (1 - TEST_SIZE)) # Train size LEARNING_RATE = 0.001 # Learning rate TRAINING_EPOCHS = 400 # Number of epochs BATCH_SIZE = 100 # Batch size DISPLAY_STEP = 20 # Display progress each x epochs HIDDEN_SIZE = 200 # Number of hidden neurons 256 ACTIVATION_FUNCTION_OUT = tf.nn.tanh # Last layer act fct STDDEV = 0.1 # Standard deviation (for weights random init) RANDOM_STATE = 100 # Random state for train_test_split print("Variables loaded successfully...\n") print("Number of predictors \t%s" %(N_INPUT)) print("Number of classes \t%s" %(N_CLASSES)) print("Number of instances \t%s" %(N_INSTANCES)) print("\n") print("Metrics displayed:\tPrecision\n") #------------------------------------------------------------------------------ # Loading data # Load data data = raw_data[KEYS].get_values() # X data labels = raw_data[Y_LABEL].get_values() # y data # One hot encoding for labels labels_ = np.zeros((N_INSTANCES, N_CLASSES)) labels_[np.arange(N_INSTANCES), labels] = 1 # Train-test split data_train, data_test, labels_train, labels_test = train_test_split(data, labels_, test_size = TEST_SIZE, random_state = RANDOM_STATE) print("Data loaded and splitted successfully...\n") #------------------------------------------------------------------------------ # Neural net construction # Net params n_input = N_INPUT # input n labels n_hidden_1 = HIDDEN_SIZE # 1st layer n_hidden_2 = HIDDEN_SIZE # 2nd layer n_hidden_3 = HIDDEN_SIZE # 3rd layer n_hidden_4 = HIDDEN_SIZE # 4th layer n_classes = N_CLASSES # output m classes # Tf placeholders X = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) dropout_keep_prob = tf.placeholder(tf.float32) def mlp(_X, _weights, _biases, dropout_keep_prob): layer1 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])), dropout_keep_prob) layer2 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer1, _weights['h2']), _biases['b2'])), dropout_keep_prob) layer3 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer2, _weights['h3']), _biases['b3'])), dropout_keep_prob) layer4 = tf.nn.dropout(tf.nn.tanh(tf.add(tf.matmul(layer3, _weights['h4']), _biases['b4'])), dropout_keep_prob) out = ACTIVATION_FUNCTION_OUT(tf.add(tf.matmul(layer4, _weights['out']), _biases['out'])) return out weights = { 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1],stddev=STDDEV)), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2],stddev=STDDEV)), 'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3],stddev=STDDEV)), 'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4],stddev=STDDEV)), 'out': tf.Variable(tf.random_normal([n_hidden_4, n_classes],stddev=STDDEV)), } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'b3': tf.Variable(tf.random_normal([n_hidden_3])), 'b4': tf.Variable(tf.random_normal([n_hidden_4])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Build model pred = mlp(X, weights, biases, dropout_keep_prob) # Loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # softmax loss optimizer = tf.train.AdamOptimizer(learning_rate = LEARNING_RATE).minimize(cost) # Accuracy correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print("Net built successfully...\n") print("Starting training...\n") #------------------------------------------------------------------------------ # Training # Initialize variables init_all = tf.initialize_all_variables() # Launch session sess = tf.Session() sess.run(init_all) # Training loop for epoch in range(TRAINING_EPOCHS): avg_cost = 0. total_batch = int(data_train.shape[0] / BATCH_SIZE) # Loop over all batches for i in range(total_batch): randidx = np.random.randint(int(TRAIN_SIZE), size = BATCH_SIZE) batch_xs = data_train[randidx, :] batch_ys = labels_train[randidx, :] # Fit using batched data sess.run(optimizer, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob: 0.9}) # Calculate average cost avg_cost += sess.run(cost, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob:1.})/total_batch # Display progress if epoch % DISPLAY_STEP == 0: print ("Epoch: %03d/%03d cost: %.9f" % (epoch, TRAINING_EPOCHS, avg_cost)) train_acc = sess.run(accuracy, feed_dict={X: batch_xs, y: batch_ys, dropout_keep_prob:1.}) print ("Training accuracy: %.3f" % (train_acc)) print ("End of training.\n") print("Testing...\n") #------------------------------------------------------------------------------ # Testing test_acc = sess.run(accuracy, feed_dict={X: data_test, y: labels_test, dropout_keep_prob:1.}) print ("Test accuracy: %.3f" % (test_acc)) sess.close() print("Session closed!")