检索对象'int' KERAS 中不是可迭代错误的问题

Question

我正在尝试运行以下代码。以下代码在googlecolab上运行良好，但在我的系统上它抛出了一个错误。我的系统上安装的Tensorflow版本是1.12.0，keras版本是2.2.4。我们非常感谢您的帮助。

def profiler(layer, test_input):
data_input = test_input
start = time.time()
data_input = layer.predict(data_input)
end = time.time() - start
milliseconds = end * 1000
return milliseconds 
def dense_layer(input_dim, dense_size):
x = tf.keras.layers.Input((input_dim))
dense = tf.keras.layers.Dense(dense_size)(x)
model = tf.keras.models.Model(inputs=x, outputs=dense)
return model
def process_config(config):
tokens = config.split(",")
values = []
for token in tokens:
token = token.strip()
if token.find("-") == -1:
token = int(token)
values.append(token)
else:
start,end = token.split("-")
start = int(start.strip())
end = int(end.strip())
values = values + list(range(start,end+1))
return values
def evaluate_dense(input_shapes_range, dense_size_range):
for input_shape in input_shapes_range:
for dense_size in dense_size_range:
to_write = open("dense_data.csv", "a+")
model = dense_layer(input_shape, dense_size)
random_input = np.random.randn(1, input_shape)
running_time = profiler(model, random_input)
del model
input_size = "2000"
dense_size = "1000, 4096"
input_size_range = process_config(input_size)
dense_size_range = process_config(dense_size)
evaluate_dense(input_size_range, dense_size_range)

错误跟踪

File "C:/Users/Dense-layer.py", line 59, in <module>
evaluate_dense(input_size_range, dense_size_range)
File "C:/Users/Dense-layer.py", line 44, in evaluate_dense
model = dense_layer(input_shape, dense_size)
File "C:/Users/Dense-layer.py", line 16, in dense_layer
x = tf.keras.layers.Input((input_dim))
File "C:Userslearnminiconda3envstensorflowlibsite-packagestensorflowpythonkerasengineinput_layer.py", line 229, in Input
input_tensor=tensor)
File "C:Userslearnminiconda3envstensorflowlibsite-packagestensorflowpythonkerasengineinput_layer.py", line 91, in __init__
batch_input_shape = (batch_size,) + tuple(input_shape)
TypeError: 'int' object is not iterable

Answer 1

input_shape应该是元组，但input_dim是整数。您已经传递了input_dim，由于您没有按名称指定它，因此它将其视为input_shape。所以，只需指定名称：

tf.keras.layers.Input(input_dim=input_dim)

或者，如果你想指定形状，可以像一样使用

tf.keras.layers.Input((input_dim,))