问题
尝试堆叠Conv时,我遇到了不兼容的形状错误 – > Lstm – >用于音频回归任务的完全连接的层.我无法弄清楚为什么我得到了我得到的错误 – 图表构建正常然后抛出错误 – 任何人都可以帮忙吗？
码
lstm_num_hidden = 128
lstm_number_layers = 3
x = tf.placeholder(tf.float32, [None, 1024])
y = tf.placeholder(tf.float32, [None, 155])
keep_probability = tf.placeholder(tf.float32)
def conv2d(x, weights):
return tf.nn.Conv2d(x, weights, strides=[1, 1, 1, 1], padding='SAME')
x_spectrogram = tf.reshape(x, [-1, 32, 32, 1])
conv1_weights = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
conv1_bias = tf.Variable(tf.constant(0.1, shape=[32]))
conv1_hidden = tf.nn.relu(conv2d(x_spectrogram, conv1_weights) + conv1_bias)
conv1_pooling = tf.nn.max_pool(conv1_hidden, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
conv2_weights = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
conv2_bias = tf.Variable(tf.constant(0.1, shape=[64]))
conv2_hidden = tf.nn.relu(conv2d(conv1_pooling, conv2_weights) + conv2_bias)
conv2_pooling = tf.nn.max_pool(conv2_hidden, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
conv2_output = tf.reshape(conv2_pooling, [-1, 64, 64])
# changes to [8, BatchSize, 8, 64]
tr_x = tf.transpose(conv2_output, [1, 0, 2])
re_x = tf.reshape(tr_x, [-1, 64])
sp_x = tf.split(0, 64, re_x)
lstm_cell = rnn_cell.LSTMCell(lstm_num_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_cell = rnn_cell.MultiRNNCell([lstm_cell] * lstm_number_layers, state_is_tuple=True)
init_state = lstm_cell.zero_state(128, tf.float32)
lstm_output, _ = rnn.rnn(cell=lstm_cell, inputs=sp_x, dtype=tf.float32, initial_state=init_state)
lstm_weights = tf.Variable(tf.truncated_normal([lstm_num_hidden, 155], stddev=0.1))
lstm_bias = tf.Variable(tf.truncated_normal([155], stddev=0.1))
out = tf.add(tf.matmul(lstm_output[-1], lstm_weights), lstm_bias)
fully_connected1_weights = tf.Variable(tf.truncated_normal([155, 1024], stddev=0.1))
fully_connected1_biases = tf.Variable(tf.truncated_normal([1024], stddev=0.1))
fully_connected1 = tf.nn.relu(tf.matmul(out, fully_connected1_weights) + fully_connected1_biases)
fully_connected1_dropout = tf.nn.dropout(fully_connected1, keep_probability)
fully_connected2_weights = tf.Variable(tf.truncated_normal([1024, 155], stddev=0.1))
fully_connected2_biases = tf.Variable(tf.truncated_normal([155], stddev=0.1))
prediction = tf.matmul(fully_connected1_dropout, fully_connected2_weights) + fully_connected2_biases
def error(labels, prediction):
return tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, prediction))))
rmse = error(y, prediction)
optimise = tf.train.AdamOptimizer(1e-4).minimize(rmse)
# Get training and testing batch.
train_batch_x = np.load("train_x.npy")
train_batch_y = np.load("train_y.npy")
test_batch_x = np.load("test_x.npy")
test_batch_y = np.load("test_y.npy")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(100):
for batch in trange(99, desc="Training"):
start = batch * 100
end = batch * 100 + 100
sess.run(optimise, { x: train_batch_x[start:end],
y: train_batch_y[start:end],
keep_probability: 0.5 })
rmse_error = sess.run(rmse, { x: test_batch_x,
y: test_batch_y,
keep_probability: 1.0 })
print "Root Mean Squared Error:" + str(rmse_error)
W tensorflow/core/framework/op_kernel.cc:975] Invalid argument: Incompatible shapes: [100,155] vs. [128,155]
[[Node: gradients/Sub_grad/broadcastGradientArgs = broadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
W tensorflow/core/framework/op_kernel.cc:975] Invalid argument: Incompatible shapes: [100,155] vs. [128,155]
[[Node: gradients/Sub_grad/broadcastGradientArgs = broadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
Traceback (most recent call last):
File "conv_rnn_experiment.py", line 81, in <module>
keep_probability: 0.5 })
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
run_Metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
Feed_dict_string, options, run_Metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
target_list, options, run_Metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [100,155] vs. [128,155]
[[Node: gradients/Sub_grad/broadcastGradientArgs = broadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
Caused by op u'gradients/Sub_grad/broadcastGradientArgs', defined at:
File "conv_rnn_experiment.py", line 63, in <module>
optimise = tf.train.AdamOptimizer(1e-4).minimize(rmse)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 269, in minimize
grad_loss=grad_loss)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 335, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 482, in gradients
in_grads = grad_fn(op, *out_grads)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 594, in _SubGrad
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 390, in _broadcast_gradient_args
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
self._traceback = _extract_stack()
...which was originally created as op u'Sub', defined at:
File "conv_rnn_experiment.py", line 62, in <module>
rmse = error(y, prediction)
File "conv_rnn_experiment.py", line 60, in error
return tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, prediction))))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 2758, in sub
result = _op_def_lib.apply_op("Sub", x=x, y=y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Incompatible shapes: [100,155] vs. [128,155]
[[Node: gradients/Sub_grad/broadcastGradientArgs = broadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
解决方法:
如果你在执行sess.run()之前打印了几个东西,你会注意到它在lstm_output处断开.关闭它,你可以开始缩小你的问题,最终成为这一行：
init_state = lstm_cell.zero_state(128,tf.float32)
此初始化用于确定批处理大小.由于您有155个单位并且声明批量大小为128,因此预计输入为128 x 155.但是,您的批次似乎是100,所以如果您更改该行,它应该可以工作.
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