x_train = x_train[..., tf.newaxis].astype("float32") x_test = x_test[..., tf.newaxis].astype("float32") train_ds = tf.data.Dataset.from_tensor_slices( (x_train, y_train)).shuffle(10000).batch(32) test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(3...
returnseq[:, tf.newaxis, tf.newaxis, :]# (batch_size, 1, 1, seq_len) defcreate_look_ahead_mask(size): mask =1- tf.linalg.band_part(tf.ones((size, size)),-1,0) returnmask# (seq_len, seq_len) defscaled_dot_pr...
waveform = tf.squeeze(audio, axis=-1)input_len = 16000waveform = waveform[:input_len]zero_padding = tf.zeros([16000] - tf.shape(waveform),dtype=tf.float32)waveform = tf.cast(waveform, dtype=tf.float32)equal_length = tf.concat([waveform, zero_padding], 0)spectrogram = tf.signal.stft...
'class_ids': predicted_classes[:, tf.newaxis], 'probabilities': tf.nn.softmax(logits), 'logits': logits, } return tf.estimator.EstimatorSpec(mode, predictions=predictions) # 计算代价. loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # 计算精确度. accuracy = tf...
mnist=tf.keras.datasets.mnist(x_train,y_train),(x_test,y_test)=mnist.load_data()x_train,x_test=x_train/255.0,x_test/255.0# Add a channels dimensionx_train=x_train[...,tf.newaxis]x_test=x_test[...,tf.newaxis] 使用tf.data来将数据集切分为batch以及混淆数据集: ...
if tf.random.uniform(()) > 0.5: input_image = tf.image.flip_left_right(input_image) input_mask = tf.image.flip_left_right(input_mask) input_image, input_mask = normalize(input_image, input_mask) return input_image, input_mask
newaxis, ...] # yp.shape == (M, ) mu_post = tf.squeeze(self.mapping_layer_mu(yp)) sigma_post = tf.squeeze(self.mapping_layer_sigma(yp)) # mu_post.shape, sigma_post.shape == (K, ) return mu_post, sigma_post y = tf.random.normal((batch_size, Ns)) factor_encoder = ...
mnist = tf.keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images, test_images = train_images / 255.0, test_images / 255.0 model.fit(train_images[..., tf.newaxis], train_labels, epochs=5) ...
, tf.newaxis] train_data = mnist_train.map(scale).shuffle(BUFFER_SIZE).batch(BATCH_SIZE) return train_data.repeat() # Define train & eval specs train_spec = tf.estimator.TrainSpec(input_fn=input_fn, max_steps=STEPS_PER_EPOCH * NUM_EPOCHS) eval_spec = tf.estimator.EvalSpec(input_fn=...
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt tf.compat.v1.disable_eager_execution() #3-1非线性回归 #使用numpy生成200个随机点,200行1列 x_data=np.linspace(-0.5,0.5,200)[:,np.newaxis] noise=np.random.normal(0,0.02,x_data.shape) ...