形状必须是等级 1 但等级 2 tflearn 错误
Shape must be rank 1 but is rank 2 tflearn error
我正在使用 tflearn 提供的 DNN 从一些数据中学习。我的 data 变量的形状为 (6605, 32)，我的 labels 数据的形状为 (6605,)，我在下面的代码中将其重塑为 (6605, 1)...
# Target label used for training
labels = np.array(data[label], dtype=np.float32)
# Reshape target label from (6605,) to (6605, 1)
labels = tf.reshape(labels, shape=[-1, 1])
# Data for training minus the target label.
data = np.array(data.drop(label, axis=1), dtype=np.float32)
# DNN
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='softmax')
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
这给了我几个错误，第一个是...
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].
...第二个是...
During handling of the above exception, another exception occurred:
ValueError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].
我不知道 rank 1 和 rank 2 是什么，所以我不知道如何解决这个问题。
在Tensorflow中，秩是张量的维数（与矩阵秩不同）。例如，以下张量的秩为 2.
t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(t1.shape) # prints (3, 3)
此外，以下张量的秩为 3。
t2 = np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
print(t2.shape) # prints (2, 2, 3)
由于 tflearn 构建于 Tensorflow 之上，输入不应是张量。我已按如下方式修改了您的代码，并在必要时进行了评论。
# Target label used for training
labels = np.array(data[label], dtype=np.float32)
# Reshape target label from (6605,) to (6605, 1)
labels =np.reshape(labels,(-1,1)) #makesure the labels has the shape of (?,1)
# Data for training minus the target label.
data = np.array(data.drop(label, axis=1), dtype=np.float32)
data = np.reshape(data,(-1,32)) #makesure the data has the shape of (?,32)
# DNN
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='softmax')
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
希望对您有所帮助。
我正在使用 tflearn 提供的 DNN 从一些数据中学习。我的 data 变量的形状为 (6605, 32)，我的 labels 数据的形状为 (6605,)，我在下面的代码中将其重塑为 (6605, 1)...
# Target label used for training
labels = np.array(data[label], dtype=np.float32)
# Reshape target label from (6605,) to (6605, 1)
labels = tf.reshape(labels, shape=[-1, 1])
# Data for training minus the target label.
data = np.array(data.drop(label, axis=1), dtype=np.float32)
# DNN
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='softmax')
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
这给了我几个错误，第一个是...
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].
...第二个是...
During handling of the above exception, another exception occurred:
ValueError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].
我不知道 rank 1 和 rank 2 是什么，所以我不知道如何解决这个问题。
在Tensorflow中，秩是张量的维数（与矩阵秩不同）。例如，以下张量的秩为 2.
t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(t1.shape) # prints (3, 3)
此外，以下张量的秩为 3。
t2 = np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
print(t2.shape) # prints (2, 2, 3)
由于 tflearn 构建于 Tensorflow 之上，输入不应是张量。我已按如下方式修改了您的代码，并在必要时进行了评论。
# Target label used for training
labels = np.array(data[label], dtype=np.float32)
# Reshape target label from (6605,) to (6605, 1)
labels =np.reshape(labels,(-1,1)) #makesure the labels has the shape of (?,1)
# Data for training minus the target label.
data = np.array(data.drop(label, axis=1), dtype=np.float32)
data = np.reshape(data,(-1,32)) #makesure the data has the shape of (?,32)
# DNN
net = tflearn.input_data(shape=[None, 32])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 1, activation='softmax')
net = tflearn.regression(net)
# Define model.
model = tflearn.DNN(net)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
希望对您有所帮助。