Keras CNN 卡在 CIFAR10 数据集的 epoch 1

Question

I want to train a CNN which classifies the images from the CIFAR10 data base. Based on previous tasks with Keras the code should work like this

import numpy as np
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from random import sample
import tensorflow as tf
from tensorflow import keras

cat_dict = {0: 'airplane',
            1: 'automobile',
            2: 'bird',
            3: 'cat',
            4: 'deer',
            5: 'dog',
            6: 'frog',
            7: 'horse',
            8: 'ship',
            9: 'truck'}

def assign_class(val):
    isclass = cat_dict[int(val)]
    return isclass

def show_imgs(X,Y):
    plt.figure(1, figsize=(20,20))
    k = 0
    for i in range(0,5):
        for j in range(0,5):
            plt.subplot2grid((5,5),(i,j))
            plt.imshow(X[k], cmap='gray')
            plt.title(assign_class(Y[k]))
            k = k+1
            plt.axis('off')
    # show the plot
    plt.show()
    
# Load data & split data between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

show_imgs(x_train, y_train)

print('Shape of training data:', x_train.shape)
print('Shape of test data:', x_test.shape)

# Normalization
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255.0
x_test /= 255.0

#One-hot encode
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

print(x_train.shape[0], 'training samples')
print(x_test.shape[0], 'test samples')
print(y_train.shape[0], 'training label samples')
print(y_test.shape[0], 'test label samples')

E = 5   #epochs
B = 128 #batch size
n_classes = 10

cnn = keras.models.Sequential()

cnn.add(keras.layers.Conv2D(filters = 32, kernel_size = (2,2), input_shape = (32,32,3), padding = 'valid', strides = (1,1) ))
cnn.add(keras.layers.Activation('relu'))
cnn.add(keras.layers.MaxPooling2D(pool_size = (2, 2))) 
cnn.add(keras.layers.Flatten())
cnn.add(keras.layers.Dense(n_classes, input_shape = (3072,), activation='softmax'))

cnn.summary()

cnn.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

log = cnn.fit(x_train, y_train, batch_size = B, epochs = E, validation_data = (x_test, y_test), verbose = 1)

I would expect that the .fit() starts to fit the model but instead the only output I get is "Epoch 1/5 " and nothing else (besides my computer starts to sound like an A747).

Answer 1

Reinstalled a whole new environment and it worked with no problem