Keras loss is nan when using inputing data from csv with numpy - python-2.7

Question

I'm trying to use the TensorFlow's Boston housing price example to learn how to use TensorFlow/Keras for regressions, but I keep running into a problem using my own data, even when I make as small of changes as possible. After giving up on writing everything myself, I simply changed the two lines of the code that input the data:
boston_housing = keras.datasets.boston_housing
(train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()
to something that, after looking online, should also create numpy arrays from my csv:
np_array = genfromtxt('trainingdata.csv', delimiter=',')
np_array = np.delete(np_array, (0), axis=0) # Remove header
test_np_array = np_array[:800,:]
tr_np_array = np_array[800:,:] # Separate out test and train data
train_labels = tr_np_array[:, 20] # Get the last row for the labels
test_labels = test_np_array[:, 20]
train_data = np.delete(tr_np_array, (20), axis=1)
test_data = np.delete(test_np_array, (20), axis=1) # Remove the last row so the data is only the features
Everything I can look at seems right – the shapes of the arrays are all correct, the arrays do seem to be correct-looking numpy arrays, the features do seem to become normalized, etc. and yet when I set verbose to 1 on model.fit(...), the very first lines of output show a problem with loss:
Epoch 1/500
32/2560 [..............................] - ETA: 18s - loss: nan - mean_absolute_error: nan
2016/2560 [======================>.......] - ETA: 0s - loss: nan - mean_absolute_error: nan
2560/2560 [==============================] - 0s 133us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan
I'm especially confused because every other place on stack overflow where I've seen the "TensorFlow loss is 'NaN'" error, it has generally a) been with a custom loss function, and b) once the model has trained for a while, not (as here) within the first 52 passes. Where that's not the case, it's because the data wasn't normalized, but I do that later in the code, and the normalization works for the housing pricing example and prints out numbers clustered around 0. At this point, my best guess is that it's a problem with the genfromtxt command, but if anyone can see what I'm doing wrong or where I might find my issue, I'd be incredibly appreciative.
Edit:
Here is the full code for the program. Commenting out lines 13 through 26 and uncommenting lines 10 and 11 make the program work perfectly. Commenting out lines 13 and 14 and uncommenting 16 and 17 was my attempt at using pandas, but that led to the same errors.
import tensorflow as tf
from tensorflow import keras
import numpy as np
from numpy import genfromtxt
import pandas as pd
print(tf.__version__)
# boston_housing = keras.datasets.boston_housing # Line 10
# (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()
np_array = genfromtxt('trainingdata.csv', delimiter=',') # Line 13
np_array = np.delete(np_array, (0), axis=0)
# df = pd.read_csv('trainingdata.csv') # Line 16
# np_array = df.get_values()
test_np_array = np_array[:800,:]
tr_np_array = np_array[800:,:]
train_labels = tr_np_array[:, 20]
test_labels = test_np_array[:, 20]
train_data = np.delete(tr_np_array, (20), axis=1)
test_data = np.delete(test_np_array, (20), axis=1) # Line 26
order = np.argsort(np.random.random(train_labels.shape))
train_data = train_data[order]
train_labels = train_labels[order]
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std
labels_mean = train_labels.mean(axis=0)
labels_std = train_labels.std(axis=0)
train_labels = (train_labels - labels_mean) / labels_std
test_labels = (test_labels - labels_mean) / labels_std
def build_model():
model = keras.Sequential([
keras.layers.Dense(64, activation=tf.nn.relu,
input_shape=(train_data.shape[1],)),
keras.layers.Dense(64, activation=tf.nn.relu),
keras.layers.Dense(1)
])
optimizer = tf.train.RMSPropOptimizer(0.001)
model.compile(loss='mse',
optimizer=optimizer,
metrics=['mae'])
return model
model = build_model()
model.summary()
EPOCHS = 500
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)
history = model.fit(train_data, train_labels, epochs=EPOCHS,
validation_split=0.2, verbose=1,
callbacks=[early_stop])
[loss, mae] = model.evaluate(test_data, test_labels, verbose=0)
print("Testing set Mean Abs Error: ${:7.2f}".format(mae * 1000 * labels_std))