My data is a (83，104） pandas dataframe. And my first(only) hidden layer is expected to have 5 neurons.
When I wrote my code like this:
input_img = Input(shape=(104,)) # 104 dates as variable
encoded = Dense(5, activation='relu')(input_img)
decoded = Dense(104, activation='relu')(encoded)
autoencoder = Model(input_img, decoded)
autoencoder.compile(loss='mean_squared_error', optimizer='sgd')
autoencoder.fit(train_data, train_data, epochs=50)
I receive error message:
ValueError: Shape mismatch: x has 32 cols (and 83 rows) but y has 104 rows (and 5 cols)
Apply node that caused the error: Dot22(/input_24, dense_47/kernel)
Toposort index: 2
Inputs types: [TensorType(float32, matrix), TensorType(float32, matrix)]
Inputs shapes: [(83, 32), (104, 5)]
Inputs strides: [(4, 332), (20, 4)]
I have followed the guidance from https://blog.keras.io/building-autoencoders-in-keras.html.
Does anyone know what is wrong here?
Thanks,
Related
training = []
bos_cikti = [0] * len(siniflar)
for belge in belgeler:
bag = []
kelime_patternleri = belge[0]
kelime_patternleri = [ayristirici.lemmatize(kelime.lower()) for kelime in kelime_patternleri]
for kelime in kelimeler:
bag.append(1) if kelime in kelime_patternleri else bag.append(0)
cikti_sira = list(bos_cikti)
cikti_sira[siniflar.index(belge[1])] = 1
training.append([bag, cikti_sira])
random.shuffle(training)
training = np.array(training) ------> This line Wrong
It shows the error on this line
train_x = list(training[:, 0])
train_y = list(training[:, 1])
#Neural Network (YSA)
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(64, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation="softmax"))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
model.save("chatbot_model.model")
print("Done")
Super confused. When I run my code, I keep on getting this error:
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
Traceback (most recent call last):
File "C:\Users\Elvan Ersöz\Desktop\Python\Bitirme Projesi\training.py", line 55, in <module>
training = np.array(training)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (20, 2) + inhomogeneous part.
I am creating a project using neural networks and natural language processing. While I'm writing my model
from line 55 I get the following error: ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (20, 2) + inhomogeneous part.
enter image description here
I am making an image segmentation transfer learning project using Pytorch. I am using the weights of this pre-trained model and class UNet3D.
https://github.com/MrGiovanni/ModelsGenesis
When I run the following codes I get this error at the line which MSELoss is called: "AttributeError: 'DataParallel' object has no attribute 'size' ".
When I delete the first line I get a similar error: "AttributeError: 'UNet3D' object has no attribute 'size'
"
How can I convert DataParallel or UNet3D class to an object which MSELoss can use? I do not need DataParallel for now. I need to run the UNet3D() class for transfer learning.
model = nn.DataParallel(model, device_ids = [i for i in range(torch.cuda.device_count())])
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), conf.lr, momentum=0.9, weight_decay=0.0, nesterov=False)
scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
initial_epoch=10
for epoch in range(initial_epoch, conf.nb_epoch):
scheduler.step(epoch)
model.train()
for batch_ndx, (x,y) in enumerate(train_loader):
x, y = x.float().to(device), y.float().to(device)
pred = model
loss = criterion(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-46-20d1943b3498> in <module>
25 x, y = x.float().to(device), y.float().to(device)
26 pred = model
---> 27 loss = criterion(pred, y)
28 optimizer.zero_grad()
29 loss.backward()
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
430
431 def forward(self, input, target):
--> 432 return F.mse_loss(input, target, reduction=self.reduction)
433
434
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in mse_loss(input, target, size_average, reduce, reduction)
2528 mse_loss, tens_ops, input, target, size_average=size_average, reduce=reduce,
2529 reduction=reduction)
-> 2530 if not (target.size() == input.size()):
2531 warnings.warn("Using a target size ({}) that is different to the input size ({}). "
2532 "This will likely lead to incorrect results due to broadcasting. "
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
592 return modules[name]
593 raise AttributeError("'{}' object has no attribute '{}'".format(
--> 594 type(self).__name__, name))
595
596 def __setattr__(self, name, value):
AttributeError: 'UNet3D' object has no attribute 'size'
You have a typo on this line:
pred = model
should be
pred = model(x)
model is nn.Module object which describes the network. x, y, pred are (supposed to be) torch tensors.
Aside from this particular case, I think it would be good to think about how to solve this type of problems in general.
You saw an error (exception) on a certain line. Is the problem there, or earlier? Can you isolate the problem?
For example, if you print out the arguments you're passing to criterion(pred, y) just before the call, do they look right? (they don't)
What happens if you create a couple of tensors of the right shape just before the call and pass them instead? (works fine)
What is the error really saying? "AttributeError: 'UNet3D' object has no attribute 'size'" - well, of course it's not supposed to have a size, but why is the code trying to access it's size? Actually, why is the code even able to access that object on that line? (since the model is not supposed to be passed to the criterion function - right?)
Maybe useful further reading: https://ericlippert.com/2014/03/05/how-to-debug-small-programs/
I have a model fitted with data but having trouble using the predict function.
d = {'df_Size': [1, 3, 5, 8, 10, 15, 18], 'RAM': [3676, 6532, 9432, 13697, 16633, 23620, 27990]}
df = pd.DataFrame(data=d)
df
X = np.array(df['df_Size']).reshape(-1, 1)
y = np.array(df['RAM']).reshape(-1, 1)
model = LinearRegression()
model.fit(X, y)
print(regr.score(X, y))
then when I try to predict on
X_Size = 25
X_Size
prediction = model.predict(X_Size)
I get the following error
ValueError: Expected 2D array, got scalar array instead:
array=25.
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
I think I am passing the 25 in the wrong format but would just like some help on getting the response for Ram considering the 25 rows.
Thanks,
You need to pass the predictor in the same shape (basically 1 column):
X.shape
Out[11]: (7, 1)
You can do:
model.predict(np.array(25).reshape(1,1))
i am basically trying to build a deep model which consists of many convolution2d layers followed by maxpooling 2d as follows :
model.add(Convolution2D(128, 54, 7, input_shape=(1, 54, 180)))
model.add(MaxPooling2D(pool_size=(1, 3)))
model.add(Convolution2D(128, 1, 7))
model.add(MaxPooling2D(pool_size=(1, 3)))
However , i am getting the following error :
File
"/home/user/anaconda2/lib/python2.7/site-packages/keras/engine/training.py",
line 100, in standardize_input_data
str(array.shape)) Exception: Error when checking model input: expected convolution2d_input_1 to have 4 dimensions, but got array
with shape (8000, 180, 54)
Blockquote
But i am following the (samples, channels, rows, cols) norm. Why is this happening ?
It seems like your input data has the wrong shape. You should print out the shape of the data that you are feeding into the network.
It seems like your array are gray input images and they normally only use 2 dimensions because they only have 1 channel. Therefore the np array is ordered without the third dimension. Normally you have to add this by using np.reshape or allocating your array in another way. When I get an error message like yours i would try:
X # training data
X = np.transpose(X, (0, 2, 1))
X = np.reshape(X, (X.shape[0], 1, X.shape[1], X.shape[2]))
Hi I'm a beginner keras.
I'm making some model.
step 1. Input batch and word list, (BATCH_SIZE, WORD_INDEX_LIST)
step 2. Get word embeddings each words (BATCH_SIZE, WORD_LENGTH, EMBEDDING_SIZE)
step 3. Average each each word embeddings in each batch. (BATCH_SIZE, EMBEDDING_SIZE)
step 4. Repeat vector N, (BATCH_SIZE, N, EMBEDDING_SIZE)
step 5. Apply Dense Layer each time step
So, I write code.
MAX_LEN = 20 ( = WORD_INDEX_LIST)
step 1
layer_target_input = Input(shape=(MAX_LEN,), dtype="int32", name="layer_target_input")
# step2
layer_embedding = Embedding(input_dim = n_symbols+1, output_dim=vector_dim,input_length=MAX_LEN,
name="embedding", weights= [embedding_weights],trainable = False)
encoded_target = layer_embedding(layer_target_input)
# step 3
encoded_target_agg = KL.core.Lambda( lambda x: K.sum(x, axis=1) )(encoded_target)
#step 4
encoded_target_agg_repeat = KL.RepeatVector( MAX_LEN)(encoded_target_agg)
# step 5
layer_annotated_tahn = KL.Dense(output_dim=50, name="layer_tahn")
layer_annotated_tahn_td = KL.TimeDistributed(layer_annotated_tahn) (encoded_target_agg_repeat)
model = KM.Model(input=[layer_target_input], output=[ layer_annotated_tahn_td])
r = model.predict({ "layer_target_input":dev_targ}) # dev_targ = (2, 20, 300)
But, when i run this code,
result is bellow.
Traceback (most recent call last):
File "Main.py", line 127, in <module>
r = model.predict({ "layer_target_input":dev_targ})
File "/usr/local/anaconda/lib/python2.7/site-packages/Keras-1.0.7-py2.7.egg/keras/engine/training.py", line 1180, in predict
batch_size=batch_size, verbose=verbose)
File "/usr/local/anaconda/lib/python2.7/site-packages/Keras-1.0.7-py2.7.egg/keras/engine/training.py", line 888, in _predict_loop
outs[i][batch_start:batch_end] = batch_out
ValueError: could not broadcast input array from shape (30,20,50) into shape (2,20,50)
why batch size is changed?
What I have wrong?
The problem is in Lambda operator. In your case it takes a tensor of shape (batch_size, max_len, embedding_size) and is expected to produce a tensor of shape (batch_size, embedding_size). However, the Lambda op doesn't know what transformation you apply internally, and therefore during the graph compilation mistakenly assumes that the shape doesn't change, therefore assuming that the output shape is (batch_size, max_len, embedding_size). The RepeastVector that follows expects the input to be two-dimensional, but never asserts that it is the case. The way it produces the expected shape is (batch_size, num_repetitions, in_shape[1]). Since Lambda mistakenly reported its shape as (batch_size, max_len, embedding_size), RepeatVector now reports its shape as (batch_size, num_repetitions, max_len) instead of expected (batch_size, num_repetitions, embedding_size). num_repetitions in your case is the same as max_len, so RepeastVector reports its shape as (batch_size, max_len, max_len). The way TimeDistributed(Dense) works is:
Reshape((-1, input_shape[2]))
Dense()
Reshape((-1, input_shape[1], num_outputs))
By now input_shape[2] is mistakenly assumed to be max_len instead of embedding_size, but the actual tensor that is given has correct shape of (batch_size, max_len, embedding_size), so what ends up happening is:
Reshape((batch_size * embedding_size, max_len))
Dense()
Reshape((batch_size * embedding_size / max_len, max_len, num_outputs))
In your case batch_size * embedding_size / max_len happens to be 2 * 300 / 20 = 30, that's where your wrong shape comes from.
To fix it, you need to explicitly tell Lambda the shape you want it to produce:
encoded_target_agg = KL.core.Lambda( lambda x: K.sum(x, axis=1), output_shape=(vector_dim,))(encoded_target)