ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)

Installing tensorflow reported this error because the version of tensorflow was not specified.

solution:

pip install tensorflow==1.9

In order to download faster, you can add a link to the domestic mirror, and add it after the above command.

-i http://pypi.douban.com/simple/ --trusted-host pypi.douban.com

Finally successfully installed

Solve the problem that keras of the following code cannot find an error:

from tensorflow import keras

Note: Because the tensorflow version is different, there are many ways to write this sentence, such as 1.9:

from tensorflow.keras.layers import Dense

 1.3:

from tensorflow.contrib import keras  # This works on tensorflow 1.3

In short, it is useless to find a lot of them. I still have to change the version. Don't use the too low version in pycharm. I followed the online tutorial and installed Anaconda and it didn't work, and it was quite boring.

If you are also using Anaconda, then pip install directly in the terminal is the last path downloaded to such as mine by default, but it would be a bit embarrassing if you use the virtual environment again. So pay attention too.

 

 

Here is a piece of code that I ran successfully: (It may take some time to download the data set)

import tensorflow as tf
from tensorflow import keras
import numpy as np

from matplotlib import pyplot as plt

# print(tf.__version__)
#class name
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
#get data
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
#First processing
#show src pic
# plt.figure()
# # plt.imshow(train_images[0])
# # plt.colorbar()
# # plt.grid(False)
# # plt.show()
#processing
train_images = train_images / 255.0
test_images = test_images / 255.0


#training 25 pic
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
#neural model
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
#model fit
model.fit(train_images, train_labels, epochs=5)


#test accuracy
# test_loss, test_acc = model.evaluate(test_images, test_labels)
# print('Test accuracy:', test_acc)

#predict model
predictions = model.predict(test_images)
def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img, cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'blue'
  else:
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array[i], true_label[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('blue')

#predict data
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()