tensorflow的keras实现搭配dataset 之一
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tensorflow的keras实现搭配dataset,几种形式都工作!
tensorflow,keras Sequential模式下:
见代码:
from tensorflow import keras as ks import tensorflow as tf # Generate dummy data import numpy as np x_train = np.random.random((1000, 20)) y_train = ks.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10) x_test = np.random.random((100, 20)) y_test = ks.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10) batch_size = 100 steps_per_epoch = int(np.ceil(x_train.shape[0]/batch_size)) train_ds = tf.data.Dataset.from_tensor_slices((x_train,y_train)) train_ds = train_ds.batch(batch_size) # batch 能给数据集增加批维度 train_ds = train_ds.repeat() train_it = train_ds.make_one_shot_iterator() x_train_it, y_train_it = train_it.get_next() test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)) test_ds = test_ds.batch(batch_size) test_ds = test_ds.repeat() model = ks.models.Sequential() # Dense(64) is a fully-connected layer with 64 hidden units. # in the first layer, you must specify the expected input data shape: # here, 20-dimensional vectors. model.add(ks.layers.Dense(64, activation=‘relu‘, input_dim=20)) model.add(ks.layers.Dropout(0.5)) model.add(ks.layers.Dense(64, activation=‘relu‘)) model.add(ks.layers.Dropout(0.5)) model.add(ks.layers.Dense(10, activation=‘softmax‘)) sgd = ks.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss=‘categorical_crossentropy‘, optimizer=sgd, metrics=[‘accuracy‘]) # passing the data to the model with the below to style, both work model.fit(x_train_it, y_train_it, epochs=20, steps_per_epoch=steps_per_epoch) print("(+("*20,‘ ‘*4) model.fit(train_ds, epochs=20, steps_per_epoch=steps_per_epoch) score = model.evaluate(test_ds, steps=128) print(score)
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