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我想使用下面的代碼與我自己的輸入圖像,而不是mnist圖像。但是,我很難將幾個顏色的.jpg圖像輸入到一個類似於下面代碼中使用的X_train的numpy數組中。我有一個名爲數據的文件夾與另一個名爲train的文件夾,其中包含我想用作我的X_train的多個圖像。我可以爲它們生成標籤並對它們進行熱門編碼。我只是不知道如何讓所有的圖像都進入像X_train這樣的好陣列。幫幫我?我沒有看here,但我得到了一個值錯誤:當我剛剛複製並粘貼了彙總代碼時,使用序列設置數組元素。如何準備多個RGB圖像作爲CNN的numpy陣列
import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
# 7. Define model architecture
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# 8. Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 9. Fit model on training data
model.fit(X_train, Y_train,
batch_size=32, nb_epoch=10, verbose=1)
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)