目录
- CIFAR10
- MyDenseLayer
CIFAR10
MyDenseLayer
import os import tensorflow as tf from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics from tensorflow import keras os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' def preprocess(x, y): # [0, 255] --> [-1,1] x = 2 * tf.cast(x, dtype=tf.float32) / 255. - 1 y = tf.cast(y, dtype=tf.int32) return x, y batch_size = 128 # x --> [32,32,3], y --> [10k, 1] (x, y), (x_val, y_val) = datasets.cifar10.load_data() y = tf.squeeze(y) # [10k, 1] --> [10k] y_val = tf.squeeze(y_val) y = tf.one_hot(y, depth=10) # [50k, 10] y_val = tf.one_hot(y_val, depth=10) # [10k, 10] print('datasets:', x.shape, y.shape, x_val.shape, y_val.shape, x.min(), x.max()) train_db = tf.data.Dataset.from_tensor_slices((x, y)) train_db = train_db.map(preprocess).shuffle(10000).batch(batch_size) test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val)) test_db = test_db.map(preprocess).batch(batch_size) sample = next(iter(train_db)) print('batch:', sample[0].shape, sample[1].shape) class MyDense(layers.Layer): # to replace standard layers.Dense() def __init__(self, inp_dim, outp_dim): super(MyDense, self).__init__() self.kernel = self.add_variable('w', [inp_dim, outp_dim]) # self.bias = self.add_variable('b', [outp_dim]) def call(self, inputs, training=None): x = inputs @ self.kernel return x class MyNetwork(keras.Model): def __init__(self): super(MyNetwork, self).__init__() self.fc1 = MyDense(32 * 32 * 3, 256) self.fc2 = MyDense(256, 128) self.fc3 = MyDense(128, 64) self.fc4 = MyDense(64, 32) self.fc5 = MyDense(32, 10) def call(self, inputs, training=None): """inputs: [b,32,32,32,3]""" x = tf.reshape(inputs, [-1, 32 * 32 * 3]) # [b,32*32*32] --> [b, 256] x = self.fc1(x) x = tf.nn.relu(x) # [b, 256] --> [b,128] x = self.fc2(x) x = tf.nn.relu(x) # [b, 128] --> [b,64] x = self.fc3(x) x = tf.nn.relu(x) # [b, 64] --> [b,32] x = self.fc4(x) x = tf.nn.relu(x) # [b, 32] --> [b,10] x = self.fc5(x) return x network = MyNetwork() network.compile(optimizer=optimizers.Adam(lr=1e-3), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy']) network.fit(train_db, epochs=5, validation_data=test_db, validation_freq=1) network.evaluate(test_db) network.save_weights('weights.ckpt') del network print('saved to ckpt/weights.ckpt') network = MyNetwork() network.compile(optimizer=optimizers.Adam(lr=1e-3), loss=tf.losses.CategoricalCrossentropy(from_logits=True), metircs=['accuracy']) network.fit(train_db, epochs=5, validation_data=test_db, validation_freq=1) network.load_weights('weights.ckpt') print('loaded weights from file.') network.evaluate(test_db)
datasets: (50000, 32, 32, 3) (50000, 10) (10000, 32, 32, 3) (10000, 10) 0 255 batch: (128, 32, 32, 3) (128, 10) Epoch 1/5 391/391 [==============================] - 7s 19ms/step - loss: 1.7276 - accuracy: 0.3358 - val_loss: 1.5801 - val_accuracy: 0.4427 Epoch 2/5 391/391 [==============================] - 7s 18ms/step - loss: 1.5045 - accuracy: 0.4606 - val_loss: 1.4808 - val_accuracy: 0.4812 Epoch 3/5 391/391 [==============================] - 6s 17ms/step - loss: 1.3919 - accuracy: 0.5019 - val_loss: 1.4596 - val_accuracy: 0.4921 Epoch 4/5 391/391 [==============================] - 7s 18ms/step - loss: 1.3039 - accuracy: 0.5364 - val_loss: 1.4651 - val_accuracy: 0.4950 Epoch 5/5 391/391 [==============================] - 6s 16ms/step - loss: 1.2270 - accuracy: 0.5622 - val_loss: 1.4483 - val_accuracy: 0.5030 79/79 [==============================] - 1s 11ms/step - loss: 1.4483 - accuracy: 0.5030 saved to ckpt/weights.ckpt Epoch 1/5 391/391 [==============================] - 7s 19ms/step - loss: 1.7216 - val_loss: 1.5773 Epoch 2/5 391/391 [==============================] - 10s 26ms/step - loss: 1.5010 - val_loss: 1.5111 Epoch 3/5 391/391 [==============================] - 8s 21ms/step - loss: 1.3868 - val_loss: 1.4657 Epoch 4/5 391/391 [==============================] - 8s 20ms/step - loss: 1.3021 - val_loss: 1.4586 Epoch 5/5 391/391 [==============================] - 7s 17ms/step - loss: 1.2276 - val_loss: 1.4583 loaded weights from file. 79/79 [==============================] - 1s 12ms/step - loss: 1.4483 1.4482733222502697