When i'm creating model ensemble consisting of 6 models in keras i do the following:
out = keras.layers.Average(name="output")(outputs) #6 models
model = Model(input, out, name='ensemble')
i would like to set the input to be
input = Input(SHAPE, name='my_input')
but then i get the error message because from what i understand when making an avarage model it's input was automatically set to 'input_6', so i get an error: Graph disconnected: cannot obtain value for tensor Tensor("input_6:0", shape=(?, 256, 256, 1), dtype=float32). Is there a way to change model input name?
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I'm trying to develop as classifier for two classes. I've implemented the model as follows:
model = keras.models.Sequential() #using tensorflow's version of keras
model.add(keras.layers.InputLayer(input_shape = X_train_scaled[:,1].shape))
model.add(keras.layers.Dense(250,activation="relu"))
model.add(keras.layers.Dense(50,activation="relu"))
model.add(keras.layers.Dense(2,activation="softmax"))
model.summary()
# Compile the model
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = "sgd",
metrics = ["accuracy"])
The size of the inputs are
X_train_scaled[:,1].shape, y_train.shape
((552,), (552,))
The entire error message is:
ValueError: Input 0 of layer sequential_9 is incompatible with the layer:
expected axis -1 of input shape to have value 552 but received input with shape (None, 1)
What am I doing wrong here?
The error message says that you defined a model which expects as input a shape of (batch_size, 552) and you are trying to feed it an array with a shape of (batch_size, 1).
The issue is most likely with
input_shape = X_train_scaled[:,1].shape)
This should most likely be:
input_shape = X_train_scaled.shape[1:]
i.e. you want to define the shape of your model to the the shape of the features (without the number of examples). The model is then fed in mini-batches... e.g. if you do call model.fit(X_train_scaled, ...) keras will create mini-batches (of 32 examples by default) and update the model weights for each mini batch.
Also, please be aware that you defined the model to return a shape of (batch_size, 2). So y_train must have a shape of (X_train.shape[0], 2).
The question was answered by Pedro
I am trying to apply a softmax activation layer to the output of the Add() layer. I am trying to make this layer the output of my model and I am running into a few problems.
It seems Add() layer doesn't allow the usage of activations and if I do something like this:
predictions = Add()([x,y])
predictions = softmax(predictions)
model = Model(inputs = model.input, outputs = predictions)
I get:
ValueError: Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata). Found: Tensor("Softmax:0", shape=(?, 6), dtype=float32)
It has nothing to do with the Add layer, you are using K.softmax directly on Keras tensors and this won't work, you need an actual layer. You can use the Activation layer for this:
from keras.layers import Activation
predictions = Add()([x,y])
predictions = Activation("softmax")(predictions)
model = Model(inputs = model.input, outputs = predictions)
The code is
model = Sequential()
model.add(Lambda(lambda x:cv2.GaussianBlur(x, (3,3), 0), input_shape=(160, 320, 3)))
And I have got this error:
TypeError: src is not a numpy array, neither a scalar
How can I create a pipeline of image processing functions to my input image. Should I create a user defined function and then add it to my layer. If so , kindly show an example
There are several good answers for getting the output of a intermediate layer in keras model. But I want to extract one layer in a keras model, and use the layer's input as the new model's input, output as the new model's output. And I have tried:
extractor = Model(model.get_layer('dw_conv5').input, model.get_layer('dw_conv5').output)
But there is an error:
Input layers to a Model must be InputLayer objects. Received inputs: Tensor("leaky_re_lu_4/LeakyRelu/Maximum:0", shape=(?, 3, 3, 256), dtype=float32). Input 0 (0-based) originates from layer type LeakyReLU.
inputs = Input(a_compatible_shape)
outputs = model.get_layer('dw_conv5')(inputs)
model = Model(inputs,outputs)
I'm a bit new to Keras and deep learning. I'm currently trying to replicate this paper but when I'm compiling the second model (with the LSTMs) I get the following error:
"TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'"
The description of the model is this:
Input (length T is appliance specific window size)
Parallel 1D convolution with filter size 3, 5, and 7
respectively, stride=1, number of filters=32,
activation type=linear, border mode=same
Merge layer which concatenates the output of
parallel 1D convolutions
Bidirectional LSTM consists of a forward LSTM
and a backward LSTM, output_dim=128
Bidirectional LSTM consists of a forward LSTM
and a backward LSTM, output_dim=128
Dense layer, output_dim=128, activation type=ReLU
Dense layer, output_dim= T , activation type=linear
My code is this:
from keras import layers, Input
from keras.models import Model
def lstm_net(T):
input_layer = Input(shape=(T,1))
branch_a = layers.Conv1D(32, 3, activation='linear', padding='same', strides=1)(input_layer)
branch_b = layers.Conv1D(32, 5, activation='linear', padding='same', strides=1)(input_layer)
branch_c = layers.Conv1D(32, 7, activation='linear', padding='same', strides=1)(input_layer)
merge_layer = layers.Concatenate(axis=-1)([branch_a, branch_b, branch_c])
print(merge_layer.shape)
BLSTM1 = layers.Bidirectional(layers.LSTM(128, input_shape=(8,40,96)))(merge_layer)
print(BLSTM1.shape)
BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)
dense_layer = layers.Dense(128, activation='relu')(BLSTM2)
output_dense = layers.Dense(1, activation='linear')(dense_layer)
model = Model(input_layer, output_dense)
model.name = "lstm_net"
return model
model = lstm_net(40)
After that I get the above error. My goal is to give as input a batch of 8 sequences of length 40 and get as output a batch of 8 sequences of length 40 too. I found this issue on Keras Github LSTM layer cannot connect to Dense layer after Flatten #818 and there #fchollet suggests that I should specify the 'input_shape' in the first layer which I did but probably not correctly. I put the two print statements to see how the shape is changing and the output is:
(?, 40, 96)
(?, 256)
The error occurs on the line BLSTM2 is defined and can be seen in full here
Your problem lies in these three lines:
BLSTM1 = layers.Bidirectional(layers.LSTM(128, input_shape=(8,40,96)))(merge_layer)
print(BLSTM1.shape)
BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)
As a default, LSTM is returning only the last element of computations - so your data is losing its sequential nature. That's why the proceeding layer raises an error. Change this line to:
BLSTM1 = layers.Bidirectional(layers.LSTM(128, return_sequences=True))(merge_layer)
print(BLSTM1.shape)
BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)
In order to make the input to the second LSTM to have sequential nature also.
Aside of this - I'd rather not use input_shape in middle model layer as it's automatically inferred.