I don't understand input of RNN model. Why it show None before node size every layer. Why it is (None,1) (None,12)
This is my code.
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=1, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
This is not a RNN, it's just a fully connected network (FC or Dense).
The first dimension of every tensor in a Keras network is the batch_size, which represents the number of "samples" or "examples" you are passing to the model. The value is None because this dimension is not fixed, you can have batches of any size you want.
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Below is my code. I'm looking to get a direction prediction on price. I understand from various tutorials it is my output layer that needs to be in Sigmoid to obtain a binary prediction.
However, even though I have done so as below, my prediction still comes out as a continuous?
model = Sequential()
# Add first layer with dropout regularisation with 100 neurons and inputs shape for 1st set
model.add(LSTM(units=256, input_shape = (data.shape[1],data.shape[2]), return_sequences=True))
model.add(Dropout(0.4))
# Add second layer with dropout
model.add(LSTM(units=128, return_sequences=False))
model.add(Dropout(0.4))
# Add a Dense layer
model.add(Dense(64, activation ='relu'))
# Add the output layer #as we are predicting direction, we use the sigmoid activation function
model.add(Dense(1, activation ='sigmoid'))
I am doing video classification for action detection using Keras (v.2.3.1). My model is CNN and LSTM. My dataset consists of videos of 3-7 seconds, each representing specific action. I use OpenCv to get frames from each video. But since video lengths are different, I get different number of frames for each video. However, I need to have the same number of frames for the LSTM layer. I searched a little bit and looks like padding along with a masking layer should do it, but I can’t figure out how to do this with Keras. Any help is appreciated. Here is my model:
conv_base = VGG16(weights= 'imagenet', include_top=False)
model = models.Sequential()
model.add(TimeDistributed(conv_base, input_shape = (n_timesteps, img_length, img_height, channel)))
model.add(TimeDistributed((Flatten())))
model.add(LSTM(units = lstm_cells))
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
I'm having difficulty printing the model.summary() after using the Sequential class in keras to build a structure like so:
embedding_inputs* numerical_input
\ /
\ /
-- CONCATENATE--
|
DENSE (50) #1
DENSE (50) #2
DENSE (50) #3
DENSE (50) #4
DENSE (1) #output
* embedding_inputs are a bunch of concatenated sequential models from
categorical variables. For the sake of simplicity,
let's pretend there is only one.
I know without the embedding layer(s), my model works and looks fine. But following my addition of an embedding layer and a concatenate layer, I'm told I need to build the model or that my Output tensors "must be the output of a Keras Layer."
I'm just utterly confused at this point. (I'm used to using the functional api but embarrassingly am having so much trouble with the Sequential one and would like to learn).
categorical = Sequential()
categorical.add(Embedding(
input_dim=len(df_train['mon'].astype('category').cat.categories),
output_dim=2,
input_length=1))
categorical.add(Flatten())
numeric = Sequential()
numeric.add(InputLayer(input_shape(1,len(numeric_column_names)),dtype='float32',name='numerical_in'))
model = Sequential()
model.add(Concatenate([numeric,categoric]))
model.add(Dense(50, input_dim=50, kernel_initializer='normal', activation='relu'))
model.add(Dense(50, input_dim=50, kernel_initializer='normal', activation='relu'))
model.add(Dense(50, input_dim=50, kernel_initializer='normal', activation='relu'))
model.add(Dense(50, input_dim=50, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal')) #output layer (1 number)
If I attempt to use model.summary() without a build:
ValueError: This model has not yet been built. Build the model first by calling build() or calling fit() with some data. Or specify input_shape or batch_input_shape in the first layer for automatic build.
If I attempt to use model.build() first, I get a message like:
ValueError: Output tensors to a Sequential must be the output of a Keras `Layer` (thus holding past layer metadata). Found: None
I have a model that needs to detect if a plant is dead or alive. It is only predicting one class, that data is imbalanced, but i have used weights to counter the imbalance.
I have looked at loads of questions about this problem, but none seem to work, apparently this problem occurs when overfitting, so I have used dropout. But the model still only predicts one class.
Heres the model:
model=Sequential()
# Convolutional layer / input layer
model.add(Conv2D(60, 5,5, activation='relu', input_shape=np.shape(X[1])))
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.8))
model.add(Flatten())
model.add(Dropout(0.7))
model.add(Dense(130, activation='relu'))
model.add(Dropout(0.6))
# Output layer
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, epochs=6, batch_size=32, class_weight=class_weight, validation_data=(X_test, y_test))
Usually it should predict both classes with 1: a healthy plant and 0:
an unhealthy plant
Since your problem is a binary classification and your output dimensionality is 2, you should change your activation to softmax.
model.add(Dense(2, activation='softmax'))
However, if you want to keep sigmoid just change your output layer units to 1, this way you will output how likely your input is one of the two classes with only one unit.
model.add(Dense(1, activation='sigmoid'))
Trying to add validation to model.fit, but whenever I do I get an error:
ValueError: Cannot feed value of shape (6, 4, 10) for Tensor 'lstm_input_1:0', which has shape '(32, 4, 10)'
Model:
data_dim = 10
timesteps = 4
batch_size = 32
model = Sequential()
model.add(LSTM(128, batch_input_shape=(batch_size, timesteps, data_dim), return_sequences=True, stateful=True))
model.add(LSTM(64, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(2, activation='softmax'))
sgd = SGD(lr=0.001, momentum=0.0, decay=0.0, nesterov=False)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, nb_epoch=50, batch_size=batch_size, validation_split=0.5)
What could be the error? If I remove validation_split the training works just fine. I've also tried to manually split my training set into two and add it with validation_data=(x_val, y_val) but I got the exact same error.
The issue comes from the fact that you hard code the batch_size value of your inputs. You have fixed it to 32 and then when you try and validate your model, the validation data is sent with a batch of 6 samples, this might be because you don't have enough validation data or maybe because the number of sample isn't a multiple of 32... However, I would let the batch_size free if I was you. Like this:
model.add(LSTM(128, input_shape=(timesteps, data_dim), return_sequences=True, stateful=True))
You specify input_shape instead of batch_input_shape. That way, your network will accept any size of batch, every layer down in the stream of your model are made to adapt to any batch_size if not hardcoded.
I hope this helps :)