I am trying to build a simple Convolutional NN with:
340 samples,
260 rows per sample
16 features per row.
I thought the order of the shape is (batch_size, steps, input_dim), which would mean (340, 16, 260) I believe.
Current code:
model = Sequential()
model.add(Conv1D(64, kernel_size=3, activation='relu', input_shape=(340, 16, 260)))
# model.add(Conv2D(64, 2, activation='relu'))
model.add(MaxPooling1D())
# model.add(Conv2D(128, 2, activation='relu'))
model.add(Conv1D(64, kernel_size=3, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dense(1, activation='linear'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
model.fit(xTrain, yTrain, batch_size=16, epochs=1000)
I am getting an error:
ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4
I am very lost and believe that my shapes are off. Could someone help me?
Thank you!
As mentioned in this answer, layers in Keras, accept two arguments: input_shape and batch_input_shape. The difference is that input_shape does not contain the batch size, while batch_input_shape is the full input shape, including the batch size.
Based on this, I think the specification input_shape=(340, 16, 260) tells keras to expect a 4-dimensional input, which is not what you want. The correct argument would be batch_input_shape=(340, 16, 260).
Related
I am attempting to setup my first deep learning sequential model with a small test dataset.
Unfortunately, I get the following error message when I call model.fit():
ValueError: Input 0 of layer "sequential_8" is incompatible with the layer: expected shape=(None, 160, 4000), found shape=(32, 4000)
My model is as follows
num_of_classes = 2
input_shape = (1,4000)
y_train_cat = keras.utils.to_categorical(y_train, num_of_classes)
y_test_cat = keras.utils.to_categorical(y_test, num_of_classes)
model = Sequential()
model.add(Conv1D(filters=10, kernel_size=5, input_shape=(160, 4000)))
model.add(MaxPool1D(pool_size=5))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
The data is of the following dimensions
x_train.shape is (160, 4000)
y_train_cat is (160, 2)
There are two classes.
Thank you for reading this far and your help in advance
When you give a layer the shape, is supposed to be the same of a single sample... change
model.add(Conv1D(filters=10, kernel_size=5, input_shape=(160, 4000)))
to
model.add(Conv1D(filters=10, kernel_size=5, input_shape=(4000,1)))
and it should work fine
Edit:
You probably also need to reshape your input to add a dimension:
x_train = np.expand_dims(x_train, 2)
Explanation:
consider a single element, a 1D convolution "slides" a 1D filter over your 1D element, however you can assume to have multiple channels, thus the leading "1" in the shape of the input
model = Sequential()
model.add(Conv1D(filters=4, kernel_size=(1), activation="relu", input_shape=(4,1)))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=32, kernel_size=(1), activation='relu'))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=64, kernel_size=(1), activation="relu"))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Conv1D(filters=64, kernel_size=(1), activation='relu'))
model.add(MaxPooling1D(pool_size=(1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(7, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
Hello, I'm new to building neural networks and decided to try my hand solving a multi-label classification problem. I'm take four feature values as input and giving the resulting classification as one or more of 7 categories. As such, I decided to implement the neural network as seen above.
However, upon fitting the model
model.fit(X_train, y_train, epochs = 10, validation_data = (X_test,y_test), batch_size = 64)
I receive this error:
Error when checking input: expected conv1d_92_input to have 3 dimensions, but got array with shape (415, 4)
I'm confused as to watch to do in order to get the neural network to fit to the data. The shape of feature and label data respectively are :
X_train = (414,4)
y_train = (413,7)
I believe you might find this previous stack stackoverflow post (It seems to be addressing your question) helpful: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)
I have a rnn and want to feed in a sentence with a length of 50, and have the output be the same length. (For a chatbot). Does anyone know why this error:
ValueError: Input 0 of layer lstm is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 5000]
keeps appearing? Here is the code:
def model():
model = Sequential()
model.add(Embedding(vocab_size, 100, input_length=l))
model.add(Flatten())
model.add(LSTM(100, return_sequences=True))
model.add(LSTM(100))
model.add(Dense(100, activation='relu'))
model.add(Dense(vocab_size, activation='softmax'))
model.summary()
return model
model = model()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(padded_x, padded_y, batch_size=128, epochs=100)
The shape of both arrays are 5000, 50....5000 sentences with 50 words each. They are already encoded. I first though it was because I was flattening...but this is the error before flattening:
ValueError: A target array with shape (5000, 50) was passed for an output of shape (None, 12097) while using as loss `categorical_crossentropy`. This loss expects targets to have the same shape as the output.
##BTW the vocab_size is 12097##
Don't flatten. The size of the output you're expecting is 50, so you need 50 neurons in your last dense layer.
def model():
model = Sequential()
model.add(Embedding(vocab_size, 100, input_length=l))
model.add(LSTM(100, return_sequences=True))
model.add(LSTM(100))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='softmax')) # 50 here, not 12,097
model.summary()
return model
I am trying to create a multi-channel 1D CNN for analyzing ECG signals. I have 258 12 lead ECGs with length 300 samples, so my input dimension is (258, 300, 12).
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=10, activation='relu', input_shape=(n_timesteps,n_features), padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=64, kernel_size=10, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=64, kernel_size=10, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=64, kernel_size=10, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=10, batch_size=20, verbose=1, validation_split = 0.2)
I'm running the code above, and getting the following error
ValueError: Error when checking target: expected dense_8 to have shape (2,) but got array with shape (1,)
Thanks for any help!
You are trying to train the model like this,
model.fit(X_train, y_train, epochs=10, batch_size=20, verbose=1, validation_split = 0.2)
The shape of y_train is something like (n, 1), where n is the number of samples used to train.
Now, you are building a model with last layer like this,
model.add(Dense(num_classes, activation='softmax'))
From the error message, it can be deduced that you are setting num_classes=2. So, the last layer will have 2 nodes. Such a model expects y_train to be of shape (n,2). But you are using y_train of shape (n,1).
In order to fix the error, you can change the last layer as,
num_classes = 1
model.add(Dense(num_classes, activation='sigmoid'))
Note that, the activation function should be changed to sigmoid.
So, you are solving a binary classification problem.
The error message indicates our model expects label with shape (2,) and i assume you are using num_classes=2. However, Your label is either 1 or 0 as the provided shape of label is (1,). To solve this error, you have yo change the output dense layer of your model, and the layer should have one neuron with sigmoid activation function.
model.add(Dense(num_classes, activation='sigmoid')) # num_classes=1
I have a working cnn with text
my X_train's shape is (39971, 10000) , y_train: (39971, 4)
max_words = 10000
model = Sequential()
model.add(Dense(512, input_shape=(max_words,), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
this works so far.
And this his is how I'm trying to make the RNN:
model = Sequential()
model.add(Embedding(max_words, 128))
model.add(LSTM(64, return_sequences=True, dropout=0.5, recurrent_dropout=0.5))
model.add(Dense(4, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
I can compile the model but when I try to run model.fit with my training data i get an error:
ValueError: Error when checking target: expected dense_42 to have 3 dimensions, but got array with shape (39971, 4)
What does this mean? How could I fix this?
As this suggest I might need to add input_shape but I'm not sure with what values.
The LSTM layer is currently returning the full sequence, so the output of the network has shape (batch_size, nb_timesteps, 4). I presume you want to use the last LSTM output only. If that's the case, set return_sequences=False.