我正在用一个基本示例验证我的TensorFlow(v2.2.0),Cuda(10.1)和cudnn(libcudnn7-dev_7.6.5.32-1 + cuda10.1_amd64.deb),但结果很奇怪。 。>
[如https://keras.io/examples/mnist_cnn/所示,在Keras中运行以下示例时,我获得了〜99%acc @validation。当我修改通过TensorFlow运行的导入时,我只能得到86%。
我可能会忘记一些东西。
使用tensorflow运行:
from __future__ import print_function import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras import backend as K batch_size = 128 num_classes = 10 epochs = 12 # input image dimensions img_rows, img_cols = 28, 28 # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() if K.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') 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 = tf.keras.utils.to_categorical(y_train, num_classes) y_test = tf.keras.utils.to_categorical(y_test, num_classes) model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(64, (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(num_classes, activation='softmax')) model.compile(loss=tf.keras.losses.categorical_crossentropy, optimizer=tf.optimizers.Adadelta(), metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])
可悲的是,我得到以下输出:
Epoch 2/12 469/469 [==============================] - 3s 6ms/step - loss: 2.2245 - accuracy: 0.2633 - val_loss: 2.1755 - val_accuracy: 0.4447 Epoch 3/12 469/469 [==============================] - 3s 7ms/step - loss: 2.1485 - accuracy: 0.3533 - val_loss: 2.0787 - val_accuracy: 0.5147 Epoch 4/12 469/469 [==============================] - 3s 6ms/step - loss: 2.0489 - accuracy: 0.4214 - val_loss: 1.9538 - val_accuracy: 0.6021 Epoch 5/12 469/469 [==============================] - 3s 6ms/step - loss: 1.9224 - accuracy: 0.4845 - val_loss: 1.7981 - val_accuracy: 0.6611 Epoch 6/12 469/469 [==============================] - 3s 6ms/step - loss: 1.7748 - accuracy: 0.5376 - val_loss: 1.6182 - val_accuracy: 0.7039 Epoch 7/12 469/469 [==============================] - 3s 6ms/step - loss: 1.6184 - accuracy: 0.5750 - val_loss: 1.4296 - val_accuracy: 0.7475 Epoch 8/12 469/469 [==============================] - 3s 7ms/step - loss: 1.4612 - accuracy: 0.6107 - val_loss: 1.2484 - val_accuracy: 0.7719 Epoch 9/12 469/469 [==============================] - 3s 6ms/step - loss: 1.3204 - accuracy: 0.6402 - val_loss: 1.0895 - val_accuracy: 0.7945 Epoch 10/12 469/469 [==============================] - 3s 6ms/step - loss: 1.2019 - accuracy: 0.6650 - val_loss: 0.9586 - val_accuracy: 0.8097 Epoch 11/12 469/469 [==============================] - 3s 7ms/step - loss: 1.1050 - accuracy: 0.6840 - val_loss: 0.8552 - val_accuracy: 0.8216 Epoch 12/12 469/469 [==============================] - 3s 7ms/step - loss: 1.0253 - accuracy: 0.7013 - val_loss: 0.7734 - val_accuracy: 0.8337 Test loss: 0.7734305262565613 Test accuracy: 0.8337000012397766
[当我导入Keras时,接近99.25%。我想念什么?
我正在用一个基本示例验证我的TensorFlow(v2.2.0),Cuda(10.1)和cudnn(libcudnn7-dev_7.6.5.32-1 + cuda10.1_amd64.deb),但结果很奇怪。 。在运行以下示例时...
keras和tensorflow.keras之间的优化器参数差异]]