'Tensor' オブジェクトには 'numpy' という属性がありません。

多層パーセプトロンを実装する場合

import tensorflow as tf
import numpy as np


#implement a simple DataLoader class to read MNIST dataset data
class DataLoader():
    def __init__(self):
        mnist = tf.contrib.learn.datasets.load_dataset("mnist")
        self.train_data = mnist.train.images

        self.train_labels = np.asarray(mnist.train.labels,dtype=np.int32)

        self.eval_data = mnist.test.images

        self.eval_labels = np.asarray(mnist.test.labels,dtype=np.int32)
    def get_batch(self,batch_size):
        index = np.random.randint(0,np.shape(self.train_data)[0],batch_size)
        return self.train_data[index,:],self.train_labels[index]

class MLP(tf.keras.Model):
    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)

    def call(self,inputs):
        x=self.dense1(inputs)
        x=self.dense2(x)
        return x

    def predict(self,inputs):
        logits = self(inputs)
        return tf.argmax(logits,axis=-1)

# Define some model hyperparameters
num_batches = 1000
batch_size = 50
learning_rate = 0.001

#Instantiate the model, data reading class and optimizer
model = MLP()
data_loader = DataLoader()
optimizer = tf.train.AdadeltaOptimizer(learning_rate = learning_rate)

# specific code
for batch_index in range(num_batches):
    X,y = data_loader.get_batch(batch_size)
    with tf.GradientTape() as tape:
        y_logit_pred = model(tf.convert_to_tensor(X))
        loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=y_logit_pred)
        print("batch %d:loss %d"%(batch_index, loss.numpy()))
    grads = tape.gradient(loss, model.variables)
    optimizer.apply_gradients(grads_and_vars=zip(grads,model.variables))
# validation set to test the model performance
num_eval_samples = np.shape(data_loader.eval_labels)[0]
y_pred = model.predict(data_loader.eval_data).numpy()
print("test accuracy:%f"%(sum(y_pred == data_loader.eval_labels)/num_eval_samples))

次のようなエラーが報告されます。

を追加することができます。

tf.enable_eager_execution(
    config=None,
    device_policy=None,
    execution_mode=None
)

これは問題を解決する
---------- ---------- -----
20201218 更新しました。
どのコードがどこに掲載されているかわからない人がいるので、フルコードを掲載するように書き換える

import tensorflow as tf
import numpy as np

tf.enable_eager_execution(
    config=None,
    device_policy=None,
    execution_mode=None
)

# Implement a simple DataLoader class to read MNIST dataset data
class DataLoader():
    def __init__(self):
        mnist = tf.contrib.learn.datasets.load_dataset("mnist")
        self.train_data = mnist.train.images

        self.train_labels = np.asarray(mnist.train.labels,dtype=np.int32)

        self.eval_data = mnist.test.images

        self.eval_labels = np.asarray(mnist.test.labels,dtype=np.int32)
    def get_batch(self,batch_size):
        index = np.random.randint(0,np.shape(self.train_data)[0],batch_size)
        return self.train_data[index,:],self.train_labels[index]

class MLP(tf.keras.Model):
    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)

    def call(self,inputs):
        x=self.dense1(inputs)
        x=self.dense2(x)
        return x

    def predict(self,inputs):
        logits = self(inputs)
        return tf.argmax(logits,axis=-1)

# Define some model hyperparameters
num_batches = 1000
batch_size = 50
learning_rate = 0.001

#Instantiate the model, data reading class and optimizer
model = MLP()
data_loader = DataLoader()
optimizer = tf.train.AdadeltaOptimizer(learning_rate = learning_rate)

# specific code
for batch_index in range(num_batches):
    X,y = data_loader.get_batch(batch_size)
    with tf.GradientTape() as tape:
        y_logit_pred = model(tf.convert_to_tensor(X))
        loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=y_logit_pred)
        print("batch %d:loss %d"%(batch_index, loss.numpy()))
    grads = tape.gradient(loss, model.variables)
    optimizer.apply_gradients(grads_and_vars=zip(grads,model.variables))
# validation set to test the model performance
num_eval_samples = np.shape(data_loader.eval_labels)[0]
y_pred = model.predict(data_loader.eval_data).numpy()
print("test accuracy:%f"%(sum(y_pred == data_loader.eval_labels)/num_eval_samples))

結果