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
Related
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.
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 currently have a simple LSTM model implemented in Keras, with a training set x_train of dimensions (1,18227, 98) and a test set x_test of dimensions (1,3217, 98) timesteps/features, respectively. Currently the model is training without hitch, but when I attempt to evaluate using my test set, I receive this error:
File "keras_LSTM.py", line 170, in <module>
loss, f1, precision = model.evaluate(x_test, y_test,
batch_size=batch_size)
File
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-
packages/keras/engine/training.py", line 1102, in evaluate
batch_size=batch_size)
File
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 751, in _standardize_user_data
exception_prefix='input')
File
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-
packages/keras/engine/training_utils.py", line 138, in
standardize_input_data
str(data_shape))
ValueError: Error when checking input: expected lstm_1_input to have
shape (18227, 98) but got array with shape (3217, 98)
Any help would be greatly appreciated - will provide code if needed. It should also be noted that my input shapes are 3 dimensional - however the error report omits the batch_size dimension and output a tuple of (sequence_length, feature_number).
Keras LSTM layer expects the input to be 3 dims as (batch_size, seq_length, input_dims), but you have assigned it wrongly. Try this
First reshape your data as:
(1,18227, 98) and a test set x_test of dimensions (1,3217, 98)
X_train = x_train.reshape(-1,98)
X_test = x_test.reshape(-1,98)
Now, use choose seq_length, i chose 10.
seq_length = 10
X_train1 = []
X_test1 = []
for i in range(0, X_train.shape[0] - seq_length, 1):
X_train1.append(X_train[i:i+seq_length])
X_test1.append(X_test[i:i+seq_length])
# labels.append(labels[i+seq_length-1])
import numpy as np
X_train1 = np.reshape(X_train1, (-1, seq_length, 98))
X_test1 = np.reshape(X_test1, (-1, seq_length, 98))
Now, you are good to go
input_dims = 98 # an integer
seq_length = 10 # an integer
model = Sequential()
model.add(LSTM(128, activation='relu', input_shape=(seq_length, input_dims), return_sequences=True))
You were using single sequence, for your model, which is ineffective way.
Ankish Bansal's idea of splitting the long sequence in small windows may be a good approach. But you might want to keep the entire sequence connected for some reason.
In that case, you should set your input_shape=(None,98), this way, your model accepts any sequence length. (Provided you don't use any Flatten layer or others that require fixed dimensions.)
But, if there is "more than one sequence" in your data, you should probably review everything, because the number of sequences usually should be the batch size.
I am new to Keras and I am trying to implement a model for an image captioning project.
I am trying to reproduce the model from Image captioning pre-inject architecture (The picture is taken from this paper: Where to put the image in an image captioning generator) (but with a minor difference: generating a word at each time step instead of only generating a single word at the end), in which the inputs for the LSTM at the first time step are the embedded CNN features. The LSTM should support variable input length and in order to do this I padded all the sequences with zeros so that all of them have maxlen time steps.
The code for the model I have right now is the following:
def get_model(model_name, batch_size, maxlen, voc_size, embed_size,
cnn_feats_size, dropout_rate):
# create input layer for the cnn features
cnn_feats_input = Input(shape=(cnn_feats_size,))
# normalize CNN features
normalized_cnn_feats = BatchNormalization(axis=-1)(cnn_feats_input)
# embed CNN features to have same dimension with word embeddings
embedded_cnn_feats = Dense(embed_size)(normalized_cnn_feats)
# add time dimension so that this layer output shape is (None, 1, embed_size)
final_cnn_feats = RepeatVector(1)(embedded_cnn_feats)
# create input layer for the captions (each caption has max maxlen words)
caption_input = Input(shape=(maxlen,))
# embed the captions
embedded_caption = Embedding(input_dim=voc_size,
output_dim=embed_size,
input_length=maxlen)(caption_input)
# concatenate CNN features and the captions.
# Ouput shape should be (None, maxlen + 1, embed_size)
img_caption_concat = concatenate([final_cnn_feats, embedded_caption], axis=1)
# now feed the concatenation into a LSTM layer (many-to-many)
lstm_layer = LSTM(units=embed_size,
input_shape=(maxlen + 1, embed_size), # one additional time step for the image features
return_sequences=True,
dropout=dropout_rate)(img_caption_concat)
# create a fully connected layer to make the predictions
pred_layer = TimeDistributed(Dense(units=voc_size))(lstm_layer)
# build the model with CNN features and captions as input and
# predictions output
model = Model(inputs=[cnn_feats_input, caption_input],
outputs=pred_layer)
optimizer = Adam(lr=0.0001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-8)
model.compile(loss='categorical_crossentropy',optimizer=optimizer)
model.summary()
return model
The model (as it is above) compiles without any errors (see: model summary) and I managed to train it using my data. However, it doesn't take into account the fact that my sequences are zero-padded and the results won't be accurate because of this. When I try to change the Embedding layer in order to support masking (also making sure that I use voc_size + 1 instead of voc_size, as it's mentioned in the documentation) like this:
embedded_caption = Embedding(input_dim=voc_size + 1,
output_dim=embed_size,
input_length=maxlen, mask_zero=True)(caption_input)
I get the following error:
Traceback (most recent call last):
File "/export/home/.../py3_env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1567, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 200 and 1. Shapes are [200] and [1]. for 'concatenate_1/concat_1' (op: 'ConcatV2') with input shapes: [?,1,200], [?,25,1], [] and with computed input tensors: input[2] = <1>
I don't know why it says the shape of the second array is [?, 25, 1], as I am printing its shape before the concatenation and it's [?, 25, 200] (as it should be).
I don't understand why there'd be an issue with a model that compiles and works fine without that parameter, but I assume there's something I am missing.
I have also been thinking about using a Masking layer instead of mask_zero=True, but it should be before the Embedding and the documentation says that the Embedding layer should be the first layer in a model (after the input).
Is there anything I could change in order to fix this or is there a workaround to this ?
The non-equal shape error refers to the mask rather than the tensors/inputs. With concatenate supporting masking, it need to handle mask propagation. Your final_cnn_feats doesn't have mask (None), while your embedded_caption has a mask of shape (?, 25). You can find this out by doing:
print(embedded_caption._keras_history[0].compute_mask(caption_input))
Since final_cnn_feats has no mask, concatenate will give it a all non-zero mask for proper mask propagation. While this is correct, the shape of the mask, however, has the same shape as final_cnn_feats which is (?, 1, 200) rather than (?, 1), i.e. masking all features at all time step rather than just all time step. This is where the non-equal shape error comes from ((?, 1, 200) vs (?, 25)).
To fix it, you need to give final_cnn_feats a correct/matching mask. Now I'm not familiar with your project here. One option is to apply a Masking layer to final_cnn_feats, since it is designed to mask timestep(s).
final_cnn_feats = Masking()(RepeatVector(1)(embedded_cnn_feats))
This can be correct only when not all 200 features in final_cnn_feats are zero, i.e. there is always at least one non-zero value in final_cnn_feats. With that condition, Masking layer will give a (?, 1) mask and will not mask the single time step in final_cnn_feats.
I would like to feed a neural net inputs of following shape:
Each training entry is a 2D array with dimensions 700x10. There are in total 204 training entries.
Labels is just 1-dimensional array of size 204 (binary output)
I tried to just use Dense layers:
model = Sequential()
model.add(Dense(300, activation='relu', input_shape=(700, 10)))
model.add(Dense(1, activation='sigmoid'))
But then I am getting following error (not related to input_shape on the first layer, but during validation of output):
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (204, 1)
204 - amount of training data.
Stacktrace:
model.fit(xTrain, yTrain, epochs=4, batch_size=6)
File "keras\models.py", line 867, in fit
initial_epoch=initial_epoch)
File "keras\engine\training.py", line 1522, in fit
batch_size=batch_size)
File "keras\engine\training.py", line 1382, in _standardize_user_data
exception_prefix='target')
File "keras\engine\training.py", line 132, in _standardize_input_data
What I found out while debugging Keras code:
It fails during validation before training. It validates output array.
According to the neural network structure, first Dense layer produces somehow 700, 1 dimensional output and it fails afterwards, since my output is just 1-d array with 204 in it.
How do I overcome this issue? I tried to add Flatten() after Dense() layer, but it probably affects accuracy in a bad way: I would like to keep information specific to one point from 700 array grouped.
The Dense layers works on only one dimension, the last.
If you're inputting (700,10) to it, it will output (700,units). Check your model.summary() to see this.
A simple solution is to flatten your data before applying dense:
model.add(Flatten(input_shape=(700,10)))
model.add(Dense(300,...))
model.add(Dense(1,...))
This way, the Dense layer will see a simple (7000,) input.
Now if you do want your model to understand those 2 dimensions separately, you should perhaps try more elaborated structures. What to do will depend a lot on what your data is and what you want to do, how you want your model to understand it, etc.