Question

我正在尝试从spyder中的tensorflow运行回归示例。回归示例为here，并且在colab中运行良好。但是，当我将代码复制到Spider时，它可以正常运行，但是历史记录变量中的所有值都是通过调用打印（历史）为NAN。 Spyder版本正在运行python 3.7。我尝试运行的代码如下。有什么想法我做错了吗？

from __future__ import absolute_import, division, print_function

import pathlib

import matplotlib.pyplot as plt

import pandas as pd
import seaborn as sns

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

tf.__version__

dataset_path = keras.utils.get_file("auto-mpg.data", "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
dataset_path

column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
                'Acceleration', 'Model Year', 'Origin'] 
raw_dataset = pd.read_csv(dataset_path, names=column_names,
                      na_values = "?", comment='\t',
                      sep=" ", skipinitialspace=True)

dataset = raw_dataset.copy()

dataset.tail()
dataset.isna().sum()

origin = dataset.pop('Origin')

dataset['USA'] = (origin == 1)*1.0
dataset['Europe'] = (origin == 2)*1.0
dataset['Japan'] = (origin == 3)*1.0

dataset.tail()

train_dataset = dataset.sample(frac=0.8,random_state=0)
test_dataset = dataset.drop(train_dataset.index)

sns.pairplot(train_dataset[["MPG", "Cylinders", "Displacement", "Weight"]], diag_kind="kde")

train_stats = train_dataset.describe()
train_stats.pop("MPG")
train_stats = train_stats.transpose()

train_stats

train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')

def norm(x):
    return (x - train_stats['mean']) / train_stats['std']

normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)

def build_model():
    model = keras.Sequential([layers.Dense(64, activation=tf.nn.relu, input_shape=[len(train_dataset.keys())]), layers.Dense(64, activation=tf.nn.relu), layers.Dense(1)])

    optimizer = tf.keras.optimizers.RMSprop(0.001)

    model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mean_absolute_error', 'mean_squared_error'])
    return model

model = build_model()

model.summary()

example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)
example_result

# 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 = 1000

history = model.fit(normed_train_data, train_labels,epochs=EPOCHS, validation_split = 0.2, verbose=0,callbacks=[PrintDot()])

hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail()

def plot_history(history):
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Abs Error [MPG]')
    plt.plot(hist['epoch'], hist['mean_absolute_error'],label='Train Error')
    plt.plot(hist['epoch'], hist['val_mean_absolute_error'],label = 'Val Error')
    plt.ylim([0,5])
    plt.legend()

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Square Error [$MPG^2$]')
    plt.plot(hist['epoch'], hist['mean_squared_error'],label='Train Error')
    plt.plot(hist['epoch'], hist['val_mean_squared_error'],label = 'Val Error')
    plt.ylim([0,20])
    plt.legend()
    plt.show()


plot_history(history)

spyder中的Tensor Flow回归问题

0 个答案: