AssertionError eğitim mnist veri kümesi Theano kullanıyorum ve derin öğrenme üzerindeki bir eğitici ve mnist veri kümesi sınıflandırma örneği yaşandı edildi

Question

keras ederken: Ben olsun

model.fit(X_train, y_train, batch_size = batch_size, nb_epoch = nb_epoch, show_accuracy= True, verbose = 1, validation_data = (X_test, y_test)) 

:

import matplotlib.pyplot as plt 
import numpy as np 

np.random.seed(123) 

from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation, Flatten 
from keras.layers.convolutional import Convolution2D, MaxPooling2D 
from keras.utils import np_utils 

batch_size = 128 
nb_classes = 10 # number of classes 

# input image dimensions 
img_rows, img_cols = 28, 28 

(X_train, y_train) , (X_test, y_test) = mnist.load_data() 

# reshape data 
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) # 1 is 1 channnel i.e. grayscale 
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) 

X_train = X_train.astype("float32") 
X_test = X_test.astype("float32") 

# normalization 
X_train /= 255; 
X_test /= 255; 

print("X_train shape: ", X_train.shape) 
print(X_train.shape[0], " train samples") 
print(X_test.shape[0], " test samples") 

# convert class vectors to binary class matrices 

y_train = np_utils.to_categorical(y_train, nb_classes) 
y_test = np_utils.to_categorical(y_train, nb_classes) 
print("One hot encoding: {}".format(y_train[0, :])) 

model = Sequential(); 

model.add(Convolution2D(6, 5, 5, input_shape = (1, img_rows, img_cols), border_mode = "same")) 

model.add(Activation("relu")) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Convolution2D(16, 5, 5, border_mode= "same")) 

model.add(Activation("relu")) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Convolution2D(120, 5, 5, border_mode= "same")) 

model.add(Activation("relu")) 
model.add(Dropout(0.25)) 

model.add(Flatten()) 

model.add(Dense(84)) 
model.add(Activation("relu")) 
model.add(Dropout(0.5)) 
model.add(Dense(10)) # because 10 output classes 
model.add(Activation("softmax")) 

# TRAIN 
model.compile(loss = "categorical_crossentropy", optimizer="adadelta") 
nb_epoch = 2 

model.fit(X_train, y_train, batch_size = batch_size, nb_epoch = nb_epoch, show_accuracy= True, verbose = 1, validation_data = (X_test, y_test)) 
score = model.evaluate(X_test, y_test, show_accuracy = True, verbose = 0) 
print("Test score: ", score[0]) 
print("Test accuracy: ", score[1]) 

Ancak hat üzerinde Bir istisna, "AssertionError kullanıcı kodu tarafından işlenmedi". İstisnada başka mesaj alamıyorum.

Herhangi bir yardım gelebilir. Teşekkür ederim.

Answer 1

doğru kod olmalıdır: Düzgün

import matplotlib.pyplot as plt 
import numpy as np 

np.random.seed(123) 

from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation, Flatten 
from keras.layers.convolutional import Convolution2D, MaxPooling2D 
from keras.utils import np_utils 

batch_size = 128 
nb_classes = 10 # number of classes 

# input image dimensions 
img_rows, img_cols = 28, 28 

(X_train, y_train) , (X_test, y_test) = mnist.load_data() 

# reshape data 
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) # 1 is 1 channnel i.e. grayscale 
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) 

X_train = X_train.astype("float32") 
X_test = X_test.astype("float32") 

# normalization 
X_train /= 255; 
X_test /= 255; 

print("X_train shape: ", X_train.shape) 
print(X_train.shape[0], " train samples") 
print(X_test.shape[0], " test samples") 

# convert class vectors to binary class matrices 

y_train = np_utils.to_categorical(y_train, nb_classes) 
y_test = np_utils.to_categorical(y_test, nb_classes) 
print("One hot encoding: {}".format(y_train[0, :])) 

model = Sequential(); 

model.add(Convolution2D(6, 5, 5, input_shape = (1, img_rows, img_cols), border_mode = "same")) 

model.add(Activation("relu")) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Convolution2D(16, 5, 5, border_mode= "same")) 

model.add(Activation("relu")) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Convolution2D(120, 5, 5, border_mode= "same")) 

model.add(Activation("relu")) 
model.add(Dropout(0.25)) 

model.add(Flatten()) 

model.add(Dense(84)) 
model.add(Activation("relu")) 
model.add(Dropout(0.5)) 
model.add(Dense(10)) # because 10 output classes 
model.add(Activation("softmax")) 

# TRAIN 
model.compile(loss = "categorical_crossentropy", optimizer="adadelta") 
nb_epoch = 2 

model.fit(X_train, y_train, batch_size = batch_size, nb_epoch = nb_epoch, show_accuracy= True, verbose = 1, validation_data = (X_test, y_test)) 
score = model.evaluate(X_test, y_test, show_accuracy = True, verbose = 0) 
print("Test score: ", score[0]) 
print("Test accuracy: ", score[1])