Matplotlib kills jupyter kernel after training model

Question

I have run a neural network in a Jupyter notebook and I want to plot the results (loss vs. epoch number). I can run the model without problems, but then even a simple matplotlib plot kills the kernel.

Here is the code that creates the model and data I want to use:

from keras import models
from keras import layers
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

from keras.datasets import imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data( num_words=10000)

# Change review into array
def vectorize_sequences(sequences, dimension=10000): 
    results = np.zeros((len(sequences), dimension)) # create all-zero matrix
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1. # If review has word, change that index to 1
    return results

x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
y_train = np.asarray(train_labels).astype('float32') 
y_test = np.asarray(test_labels).astype('float32')

# Create model
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,))) # two int. layers w/16 hidden units each
model.add(layers.Dense(16, activation='relu')) 
model.add(layers.Dense(1, activation='sigmoid')) # outputs the scalar prediction
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

# Create mini-test data
x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]

# fit model
history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

# Get values for plot
history_dict = history.history
history_dict.keys()
loss_values = history_dict['loss'] 
val_loss_values = history_dict['val_loss']
epoch_num = [i for i in range(1,21)]

This works as expected. However, when I try to plot the data with the code below, I get a message: "The kernel appears to have died. It will restart automatically."

plt.plot(epoch_num, loss_values, 'bo', label='Training loss') 
plt.plot(epoch_num, val_loss_values, 'b', label='Validation loss')
plt.title('Training and validation loss') 
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

I can restart the kernel and make matplotlib plots, but when I try to make a plot after running the model matplotlib causes the error to appear. I have tried updating keras, tensorflow, matplotlib, and numpy to no effect. Can anyone provide insight as to why this happens, and provide a solution?

Answer 1

I used latest tensorflow and imported keras from tensorflow. Everything worked as expected. I changed first three line as shown below. Full code is here

from tensorflow import keras
from tensorflow.keras import models
from tensorflow.keras import layers

The following plot shows epoch versus loss