使用python代码利用CNN训练鸢尾花数据集

可以使用下面的Python代码来利用卷积神经网络（CNN）训练鸢尾花数据集：from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense# Initialising the CNN
classifier = Sequential()# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))# Step 3 - Flattening
classifier.add(Flatten())# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])# Part 2 - Fitting the CNN to the imagesfrom keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)test_datagen = ImageDataGenerator(rescale = 1./255)training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)