我是张量流的新手，我的第一次尝试是线性回归。我从csv文件taxi_data = pd.read_csv("taxi_pretreatment.csv")读取数据
然后我提取我想要使用的列
data = data[["distance", "virages", "prix"]]
data_array = data.as_matrix() # to_records(index=False)
X, y = data_array[:, [0,1]], data_array[:, 2]
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=42)
m, n = X_train.shape
u, v = X_test.shape
X_train_bias = np.c_[X_train, np.float32(np.ones((m, 1)))]
X_test_bias = np.c_[X_test, np.ones((u, 1))]
然后我启动基于梯度的算法
tf.reset_default_graph()
learning_rate = 0.01
X_data = tf.placeholder(shape=[None, n+1], dtype=np.float32, name="X")
y_data = tf.placeholder(shape=[None, 1], dtype=np.float32, name="y")
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta")
y_pred = tf.matmul(tx, theta, name="predictions")
error = y_pred - ty
mse = tf.reduce_mean(tf.square(error), name="mse")
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
training_op = optimizer.minimize(mse)
n_epochs = 10
batch_size = 100
n_batches = int(np.ceil(m / batch_size))
def fetch_batch(epoch, batch_index, batch_size):
np.random.seed(epoch * n_batches + batch_index)
indices = np.random.randint(m, size=batch_size)
X_batch = X_train_bias[indices]
y_batch = y_train.reshape(-1, 1)[indices]
return X_batch, y_batch
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(n_epochs):
for batch in range(n_batches):
X_batch, y_batch = fetch_batch(epoch, batch, batch_size)
print(X_batch.ctypes)
print(y_batch.ctypes)
print()
sess.run(training_op, feed_dict={X_data: X_batch, y_data: y_batch})
if epoch % 100 == 0:
print("Epoch", epoch, "MSE =", mse.eval())
# sess.run(training_op)
# sess.run(training_op)
best_theta = theta.eval()
print("Best theta:")
print(best_theta)
该代码来自使用TensorFlow书的Hands on Machine Learning，但我不明白为什么它不起作用
这是错误
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1322 try:
-> 1323 return fn(*args)
1324 except errors.OpError as e:
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1301 feed_dict, fetch_list, target_list,
-> 1302 status, run_metadata)
1303
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
472 compat.as_text(c_api.TF_Message(self.status.status)),
--> 473 c_api.TF_GetCode(self.status.status))
474 # Delete the underlying status object from memory otherwise it stays alive
InvalidArgumentError: You must feed a value for placeholder tensor 'y_4' with dtype float and shape [?,1]
[[Node: y_4 = Placeholder[dtype=DT_FLOAT, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-93-7e2694bda819> in <module>()
12 print()
13
---> 14 sess.run(training_op, feed_dict={X_data: X_batch, y_data: y_batch})
15
16 if epoch % 100 == 0:
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
887 try:
888 result = self._run(None, fetches, feed_dict, options_ptr,
--> 889 run_metadata_ptr)
890 if run_metadata:
891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1118 if final_fetches or final_targets or (handle and feed_dict_tensor):
1119 results = self._do_run(handle, final_targets, final_fetches,
-> 1120 feed_dict_tensor, options, run_metadata)
1121 else:
1122 results = []
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1315 if handle is None:
1316 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1317 options, run_metadata)
1318 else:
1319 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1334 except KeyError:
1335 pass
-> 1336 raise type(e)(node_def, op, message)
1337
1338 def _extend_graph(self):
InvalidArgumentError: You must feed a value for placeholder tensor 'y_4' with dtype float and shape [?,1]
[[Node: y_4 = Placeholder[dtype=DT_FLOAT, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'y_4', defined at:
File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2728, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2850, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2910, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-19-1a9e79400731>", line 4, in <module>
ty = tf.placeholder(shape=(None, 1), dtype=tf.float32, name="y")
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 1599, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 3091, in _placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'y_4' with dtype float and shape [?,1]
[[Node: y_4 = Placeholder[dtype=DT_FLOAT, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
你能帮我解决一下这个错误吗？
该代码来自使用TensorFlow书的Hands on Machine Learning，但我不明白为什么它不起作用
实际上，这不完全是the code from the book。如果你仔细观察，在书中X和y是常数，并允许评估mse而不喂任何值。
在将常量更改为占位符之后，您必须继续执行它们，除非计算的张量不依赖于X和y，例如theta。 MSE显然取决于两者，因此解决方案就是这样做
mse.eval(feed_dict={X_data: X_batch, y_data: y_batch})
PS。您的代码中有什么ty？应该是y_data那里。
你批量生成函数是问题，具体来说，y_batch返回的形状不正确，检查y_batch的形状是什么，应该是（batch_size，1）