可视化不出现在Tensorflow-PyCharm IDE中

Question

我正在使用PyCharm Community Edition和Python 3.7。通过Anaconda，我安装了Tensorflow机器学习包。

我正在关注回归教程here，但我的输出有限。只有数字结果显示在输出控制台上 - 而不是视觉效果。我知道plot_history(history)语句应该导致输出控制台上的可视化 - 但是，它没有显示。如何在PyCharm上进行相应的可视化？

这是我的main.py：

from __future__ import absolute_import, division, print_function

import tensorflow as tf
from tensorflow import keras

import numpy as np
import pandas as pd

print(tf.__version__)

boston_housing = keras.datasets.boston_housing

(train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()

# Shuffle the training set
order = np.argsort(np.random.random(train_labels.shape))
train_data = train_data[order]
train_labels = train_labels[order]

# output some data
print("Training set: {}".format(train_data.shape))  # 404 examples, 13 features
print("Testing set:  {}".format(test_data.shape))  # 102 examples, 13 features

column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
                'TAX', 'PTRATIO', 'B', 'LSTAT']

df = pd.DataFrame(train_data, columns=column_names)
df.head()

print(train_labels[0:10])  # Display first 10 entries

# Test data is *not* used when calculating the mean and std
# Normalize data
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

print(train_data[0])  # First training sample, normalized


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()


# Display training progress by printing a single dot for each completed epoch
class PrintDot(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs):
        if epoch % 100 == 0: print('')
        print('.', end='')


EPOCHS = 500

# Store training stats
history = model.fit(train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0,
                    callbacks=[PrintDot()])

import matplotlib.pyplot as plt


def plot_history(history):
    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Abs Error [1000$]')
    plt.plot(history.epoch, np.array(history.history['mean_absolute_error']),
             label='Train Loss')
    plt.plot(history.epoch, np.array(history.history['val_mean_absolute_error']),
             label='Val loss')
    plt.legend()
    plt.ylim([0, 5])


plot_history(history)

[loss, mae] = model.evaluate(test_data, test_labels, verbose=0)

print("Testing set Mean Abs Error: ${:7.2f}".format(mae * 1000))

test_predictions = model.predict(test_data).flatten()

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [1000$]')
plt.ylabel('Predictions [1000$]')
plt.axis('equal')
plt.xlim(plt.xlim())
plt.ylim(plt.ylim())
_ = plt.plot([-100, 100], [-100, 100])

error = test_predictions - test_labels
plt.hist(error, bins=50)
plt.xlabel("Prediction Error [1000$]")
_ = plt.ylabel("Count")

这是我的输出：

C:UsersOwnerAnaconda3envs	ensorflowTest1python.exe C:/Users/Owner/PycharmProjects/tensorflowTest1/main.py
1.12.0
Training set: (404, 13)
Testing set:  (102, 13)
[32.  27.5 32.  23.1 50.  20.6 22.6 36.2 21.8 19.5]
[-0.39725269  1.41205707 -1.12664623 -0.25683275 -1.027385    0.72635358
 -1.00016413  0.02383449 -0.51114231 -0.04753316 -1.49067405  0.41584124
 -0.83648691]
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 64)                896       
_________________________________________________________________
dense_1 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
=================================================================
Total params: 5,121
Trainable params: 5,121
Non-trainable params: 0
_________________________________________________________________
2018-12-02 14:58:27.908127: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX

....................................................................................................
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....................................................................................................
....................................................................................................Testing set Mean Abs Error: $2717.02

Process finished with exit code 0