Визуализации таблиц не отображаются в Tensorflow-PyCharm IDE - PullRequest
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Визуализации таблиц не отображаются в Tensorflow-PyCharm IDE

0 голосов
/ 06 декабря 2018

Я использую PyCharm Community Edition и Python 3.7.Через Анаконду я установил пакет машинного обучения Tensorflow.

Я следую учебному пособию по регрессии Tensorflow Google здесь , но получаю ограниченный вывод.На выходной консоли и в отдельном окне отображаются только числовые результаты и plt соответственно.

Таблица данных, упомянутая в прикрепленной ссылке, закодированная как: # Prints the stored data table in a formatted fashion (ERROR) df = pd.DataFrame(train_data, columns = column_names) df.head() , похоже, не генерируетсвое собственное окно, как предполагает ссылка.Как отобразить эту таблицу в новом окне, так же, как отображается plt?

Вот мой main.py:

# Importing the different libraries and packages
# Machine Learning is mainly executed by the TENSORFLOW LIBRARY

from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd

TFVersion = tf.__version__
newline = "\n"

print(newline)
print("Current version of TensorFlow: ", TFVersion)

# Downloading the Boston Housing Data Set - it is already present in the keras
# NOTE: This will be referred to as the "BHD"
boston_housing = keras.datasets.boston_housing

# Initializing the training + testing data and labels as per the information suggested in the BHD
(train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()

# Shuffle the training set in order to assure randomness - this condition is required for any statistical analysis
order = np.argsort(np.random.random(train_labels.shape))
train_data = train_data[order]
train_labels = train_labels[order]

# Printing the training and testing data sets (the .shape member function gets the examples and feature frequency
# from the train_data vector instance)
print("Training set: {}".format(train_data.shape))  # 404 examples, 13 features
print("Testing set:  {}".format(test_data.shape))   # 102 examples, 13 features

# Initializing the variables/attributes for the data-set
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
                'TAX', 'PTRATIO', 'B', 'LSTAT']

# Prints the stored data table in a formatted fashion (ERROR)
df = pd.DataFrame(train_data, columns = column_names)
df.head()

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

# TEST DATA is *not* used when calculating the mean and std
# Normalize data - these are basically z-scores
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

plt.interactive(False)

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")

plt.show()
# https://www.youtube.com/watch?v=voLSnXi4hAI
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