I am a newbie in ML. I have a set of timeseries data with Date and Temp cols., that I want to use for anomaly detection. I used the MinMax scaler on the data and I got an array normal_train_data with shape (200, 0).
Then I used the autoencoder which uses
keras.layers.Dense(128, activation ='sigmoid').
After that, when I call
history = model.fit(normal_train_data, normal_train_data, epochs= 50, batch_size=128, validation_data=(train_data_scaled[:,1:], train_data_scaled[:,1:]) ...)
I get the error:
ValueaError: Dimensions must be equal but are 128 and 0 with input shapes: [?,128], [?,0].
As far as I understand the input has shape (200,0) and the output(1,128).
Can you help me to fix this error please? Thankyou
I tried to use tf.keras.layers.Flatten() in the encoder part. I am not sure if it's ok to use Dense layer or should I choose another.
Related
I am fairly new to TF, Keras and ML in general.
I am trying to implement a very simple MLP with an input shape of (batch_size,3,2) and an output shape of (batch_size,3), that is (if I got it right): for every 3x2 feature, there is a corresponding 3 value array label.
Here is how I create the model:
model = tf.keras.Sequential([
tf.keras.layers.Dense(50,tf.keras.activations.relu,input_shape=((3,2)),
tf.keras.layers.Dense(3)
])
and these are the X and y shapes:
X_train.shape,y_train.shape
TensorShape([64,3,2]),TensorShape([64,3])
On model.fit I am facing a weird error I cannot understand:
ValueError: Dimensions must be equal, but are 3 and 32 for ... with input shapes: [32,3,3] and [32,3]
I have no clue what's going on, I understand the batch size is 32, but where does that [32,3,3] comes from?
Moreover, if from the original 64, I lower the number (shapes) of X_train and y_train, say, to: (19,3,2) and (19,3), I get the following error instead:
InvalidArgumentError: required broadcastable shapes at loc(unknown)
What's even more weird for me is that if I specify a single unit for the output (last) layer, instead of 3 like this:
model = tf.keras.Sequential([
tf.keras.layers.Dense(50,tf.keras.activations.relu,input_shape=((3,2)),
tf.keras.layers.Dense(1)
])
model.fit works, but the predictions have shape (1,3,1) instead of my expected (3,)
I am very confused.
Whenever you have not any idea about the journey of data throughout your model, use model.summary() to see the details and what happens to the shape of data in each layer.
In this case, the input is a 2D array, and the output is a 1D array, and you just used dense layers. Dense layers can not handle 2d features in nature. For example for an image as input, you can not feed it directly to a dense layer. Instead you should use other layers such as Conv2D or Flatten your input (make it 1D) before feeding your data to the dense layer. Otherwise you will get the other dimension in the output.
Inference: If your input dimension and output dimension differs, somewhere in your model, the shape need to be changed. Most common ways to do so, is using a Flatten layer or GlobalAveragePooling and so on.
When you pass an input to a dense layer, the input should be flattened first. There are 2 ways to deal with this:
Way 1: Adding a flatten input as a first layer of your model:
model = Sequential()
model.add(Flatten(input_shape=(3,2)))
model.add(Dense(50, 'relu'))
model.add(Dense(3))
Way 2: Converting the 2D array to 1D before passing the inputs to your model:
X_train = tf.reshape(X_train, shape=([6]))
or
X_train = tf.reshape(X_train, shape=((6,)))
Then change the input shape of the first layer as:
model.add(Dense(50, 'relu', input_shape=(6,))
Hello I am trying to get an output of an array of 7 classes. But when I run my code it says that it expects my data output labels to have some other shape. Here is my code -
def make_model(self):
self.model.add(InceptionV3(include_top=False,
input_shape=(self.WIDTH, self.HEIGHT, 3),
weights="imagenet"))
self.model.add(Dense(7, activation='softmax'))
self.model.layers[0].trainable = False
My model compilation and fitment part
def train(self):
self.model.compile(optimizer=self.optimizer, loss='mse', metrics=['accuracy'])
self.model.fit(x=x, y=y, batch_size=64,
validation_split=0.15, shuffle=True, epochs=self.epochs,
callbacks=[self.tensorboard, self.reducelr])
I get the error -
File "model.py", line 60, in train
callbacks=[self.tensorboard, self.reducelr])
ValueError: A target array with shape (23639, 7) was passed for an output of shape (None, 6, 13, 7) while using as loss `mean_squared_error`. This loss expects targets to have the same shape as the output.
Now here it is saying that it expected (None, 6, 13, 7) however i gave it labels - (23639, 7)
Now we can clearly see that in the self.model.add(Dense(7, activation='softmax')) I have specified 7 as the number of output categories
Here is the model summary -
So can someone tell me what is wrong here
By the way i did try using categorical_crossentropy to see if it makes a difference but it didn't.
In case you wanted the full code -
Full Code
The problem is in the output of the InceptionV3... it returns 4D sequences, you need to reduce the dimensionality before the final dense layer in order to match the target dimensionality (2D). you can do this using Flatten or GlobalPooling layers.
If yours is a classification problem I also recommend you use categorical_crossentropy (if you have one-hot encoded label) or sparse_categorical_crossentropy (if u have integer encoded labels). mse is suited for regression problems
Let us say that I build an extreamly simple CNN with Keras to classify vectors.
My input (X_train) is a matrix in which each row is a vector and each column is a feature. My input labels (y_train) is matrix where each line is a one hot encoded vector. This is a binary classifier.
my CNN is built as follows:
model = Sequential()
model.add(Conv1D(64,3))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(2))
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', matrics =
['accuracy'])
model.fit(X_train,y_train,batch_size = 32)
But when I try to run this code, I get back this error message:
Input 0 is incompatible with layer conv1d_23: expected ndim=3, found
ndim=2
why would keras expect 3 dims? one dim for samples, and one for features. And more importantly, how can I fix this?
X_train is suppose to have the shape: (batch_size, steps, input_dim), see documentation. It seems like you are missing one of the dimensions.
I would guess input_dim in your case is 1 and that is why it is missing. If so, change the
model.fit
line to
model.fit(tf.expand_dims(X_train,-1), y_train,batch_size = 32)
Your code is not a minimal working example, so I am not able to verify if that is the only problem, but this should hopefully fix your current error message.
A Conv1D layer expects an input with shape (samples, width, channels), so this does not match your input data, producing an error.
The convolution operation is done on the width dimension, so assuming that you want to do convolution on what you call features, then you should reshape your data to add a dummy channels dimension with a value of one:
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
I want to feed a sparse tensor into a dense layer
inputs1 = tf.sparse_placeholder(tf.float32, shape=[None, 500], name='input1')
model1 = tf.layers.dense(inputs=inputs1, units=128, name='dense1')
When I execute this I get the following error
ValueError: The last dimension of the inputs to `Dense` should be defined. Found `None`
If I change sparse_placeholder to regular place_holder I don't get this error.
I recommend you use FeatureColumn when you try to do this. First create a column representing your sparse tensor, then build an input layer. Finally, feed this input layer to your dense layer. This will help your code make your intention clear; do you want this to be a one-hot tensor? do you want embeddings? etc.
I am trying to experiment with a multi-layer encoder-decoder type of network. The screenshot of the last several layers of network architecture is as follows. This is how I setup model compiling and training process.
optimizer = SGD(lr=0.001, momentum=0.9, decay=0.0005, nesterov=False)
autoencoder.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=['accuracy'])
model.fit(imgs_train, imgs_mask_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1,callbacks=[model_checkpoint])
imgs_train and imgs_mask_train are of shape (2000, 1, 128, 128). imgs_train represent the raw image and imgs_mask_train represents the mask image. I am trying to solve a semantic segmentation problem. However, running the program generates the following error message, (I only keep the main related part).
tensorflow.python.pywrap_tensorflow.StatusNotOK: Invalid argument: logits first dimension must match labels size. logits shape=[4096,128] labels shape=[524288]
[[Node: SparseSoftmaxCrossEntropyWithLogits = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_364, Cast_158)]]
It seems to me that the loss function of sparse_categorical_crossentropy causes the problem for the current (imgs_train, imgs_mask_train) shape setting. The Keras API does not include the detail about how to setup the target tensor. Any suggestions are highly appreciated!
I am currently trying to figure the same problem and as far as I can tell it takes a sparse representation of the target category. That means integers as the target label instead of the one-hot encoded binary class matrix.
Concerning your problem, do you have categories in your masking or do you just have information about the outline of an object? With outline information it becomes a pixel wise binary loss instead of a categorical one. If you have categories, the output of your decoder should have dimensionality (None, number_of_classes, 128, 128). On that you should be able to use a sparse target mask but I haven't tried this myself...
Hope that helps