无法将 feed_dict 键解释为张量：张量不是此图的元素

Question

试图预测 gen 的类型，但出现了一些错误，你能提出什么问题吗？ 任何帮助将不胜感激。在其他情况下，可以预测 ZOLANDO 数据集中的衣服类型。但在其他情况下，我被卡住了:(

#some code of gen1, gen2 and merged dataFrames
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
    y_train = y_train['y'].values.reshape(1,14000).astype('int32')
    labels_ = np.zeros((14000,2))
    labels_[np.arange(14000), y_train] = 1        
    X_train = np.array(X_train)
    X_train = X_train.transpose()
    X_train = np.where(X_train<0, X_train ** 2, X_train)        
    n_dim = X_train.shape[0]
    ops.reset_default_graph()
    tf.compat.v1.disable_eager_execution()
    n1 = 2
    n2 = 2
    cost_history = np.empty(shape=[1], dtype = float)
    learning_rate = tf.compat.v1.placeholder(tf.float32, shape=())
    X = tf.compat.v1.placeholder(tf.float32, shape=(n_dim, None))
    #X = np.array([n_dim, None],dtype="float32")
    Y = tf.compat.v1.placeholder(tf.float32, shape=(n2, None))
    W1 = tf.Variable(tf.random.truncated_normal([n1,n_dim], stddev=.1))
    b1 = tf.Variable(tf.zeros([n1,1]))
    W2 = tf.Variable(tf.random.truncated_normal([n2,n1], stddev=.1))
    b2 = tf.Variable(tf.zeros([n2,1]))
    
    
    Z1 = tf.nn.relu(tf.matmul(W1,X) + b1)
    Z2 = tf.nn.relu(tf.matmul(W2,Z1) + b2)
    y_ = tf.nn.softmax(Z2,0)
    cost =  - tf.reduce_mean(Y * tf.math.log(y_) + (1-Y) * tf.math.log(1-y_) )
    optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    init = tf.compat.v1.global_variables_initializer()
   
    ops.reset_default_graph()
    tf.compat.v1.disable_eager_execution()
    
    sess = tf.compat.v1.Session()
    sess.run(tf.compat.v1.global_variables_initializer())
    training_epochs = 100
    
    cost_history = []
    for epoch in range(training_epochs+1):
        sess.run(optimizer, feed_dict = {X: X_train, Y: labels_, learning_rate: 0.001})
        cost_ = sess.run(cost, feed_dict = { X: X_train, Y: labels_, learning_rate: 0.001})
        cost_history = np.append(cost_history, cost_)
    
        if (epoch % 10 == 0):
            print("Reached epoch",epoch,"cost J =", cost_)

得到：

TypeError： 无法将 feed_dict 键解释为 Tensor：Tensor Tensor("Placeholder_1:0", shape=(2, None), dtype=float32) 不是此图的元素。 p>

Answer 1

graph = tf.Graph()    
    with graph.as_default():
        (your code of tf)