tensorflow实现mnist手写数字识别报错？ - 知否问答 - 万象云+社区

def __init__(self): mnist = tf.keras.datasets.mnist.load_data(path = 'mnist.npz') self.train_data = mnist[0][0] self.train_data = np.reshape(self.train_data,(self.train_data.shape[0],28*28)) self.train_labels = mnist[0][1] self.eval_data = mnist[1][0] self.train_data = np.reshape(self.train_data,(self.train_data.shape[0],28*28)) self.eval_labels = mnist[1][1] def get_batch(self, batch_size): indexs = np.random.randint(0,self.train_data.shape[0],batch_size) return self.train_data[indexs, :], self.train_labels[indexs]

def __init__(self): super().__init__() self.dense1 = tf.keras.layers.Dense(units=100, activation= tf.nn.relu) self.dense2 = tf.keras.layers.Dense(units=10,activation =None) def call(self, inputs): x = self.dense1(inputs) y = self.dense2(x) return y def predict(self, inputs): logits = self(inputs) return tf.argmax(logits, axis=-1)

X , y = data_loader.get_batch(batch_size) print(np.shape(X)) with tf.GradientTape() as tape: X = tf.convert_to_tensor(X, dtype = tf.int64, name = 'X') print(X) y_logit_pred = model(X) loss = tf.losses.sparse_softmax_cross_entropy(labels = y, logits = y_logit_pred) print('batch %d: loss %f' % (batch_index, loss.numpy())) grads = tape.gradient(loss, model.variables) optimizer.apply_gradients(grads_and_vars = zip(grads, model.variables))

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