I need some help, I keep getting this strange situation where my Keras model goes out of range
print(np.array(train_x).shape)
print(np.array(train_y).shape)
Returns:
(731, 42)
(731,)
Then:
normalizer = Normalization(input_shape=[42,], axis=None)
normalizer.adapt(train_x[0])
linear_model = Sequential([
normalizer,
Dense(units=1)
])
linear_model.summary()
Shows:
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
normalization_5 (Normalizati (None, 42) 3
_________________________________________________________________
dense_1 (Dense) (None, 1) 43
=================================================================
Total params: 46
Trainable params: 43
Non-trainable params: 3
_________________________________________________________________
Then:
linear_model.compile(
optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')
linear_model.fit(
train_x,
train_y,
epochs=100)
Which results in an IndexError: list index out of range. It looks like my inputs are in the right shape. Any idea what could be causing this?
train_x and train_y needed to be numpy arrays.
Related
I am testing something which includes building a FCNN network Dynamically. Idea is to build Number of layers and it's neurons based on a given list and the dummy code is:
neurons = [10,20,30] # First Dense has 10 neuron, 2nd has 20 and third has 30
inputs = keras.Input(shape=(1024,))
x = Dense(10,activation='relu')(inputs)
for n in neurons:
x = Dense(n,activation='relu')(x)
out = Dense(1,activation='sigmoid')(x)
model = Model(inputs,out)
model.summary()
keras.utils.plot_model(model,'model.png')
for layer in model.layers:
print(layer.name)
To my surprise, it is showing nothing.I even compiled and ran the functions again and nothing came out.
The model.summary always shows number of trainable and non trainable params but not the model structure and layer names. Why is this happening? Or is this normal?
About model.summary(), don't mix tf 2.x and standalone keras at a time. If I ran you model in tf 2.x, I get the expected results.
from tensorflow.keras.layers import *
from tensorflow.keras import Model
from tensorflow import keras
# your code ...
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 1024)] 0
_________________________________________________________________
dense (Dense) (None, 10) 10250
_________________________________________________________________
dense_1 (Dense) (None, 10) 110
_________________________________________________________________
dense_2 (Dense) (None, 20) 220
_________________________________________________________________
dense_3 (Dense) (None, 30) 630
_________________________________________________________________
dense_4 (Dense) (None, 1) 31
=================================================================
Total params: 11,241
Trainable params: 11,241
Non-trainable params: 0
_________________________________
About plotting the model, there is a couple of option that can be used while you plot your keras model. Here is one example:
keras.utils.plot_model(model, show_dtype=True,
show_layer_names=True, show_shapes=True,
to_file='model.png')
I'm working on a basic RNN model for a multiclass task and I'm facing some issues with output dimensions.
This is my input/output shapes:
input.shape = (50000, 2, 5) # (samples, features, feature_len)
output.shape = (50000, 17, 185) # (samples, features, feature_len) <-- one hot encoded
input[0].shape = (2, 5)
output[0].shape = (17, 185)
This is my model, using Keras functional API:
inp = tf.keras.Input(shape=(2, 5,))
x = tf.keras.layers.LSTM(128, input_shape=(2, 5,), return_sequences=True, activation='relu')(inp)
out = tf.keras.layers.Dense(185, activation='softmax')(x)
model = tf.keras.models.Model(inputs=inp, outputs=out)
This is my model.summary():
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 2, 5)] 0
_________________________________________________________________
lstm (LSTM) (None, 2, 128) 68608
_________________________________________________________________
dense (Dense) (None, 2, 185) 23865
=================================================================
Total params: 92,473
Trainable params: 92,473
Non-trainable params: 0
_________________________________________________________________
Then I compile the model and run fit():
model.compile(optimizer='adam',
loss=tf.nn.softmax_cross_entropy_with_logits,
metrics='accuracy')
model.fit(x=input, y=output, epochs=5)
And I'm getting a dimension error:
ValueError: Dimensions must be equal, but are 17 and 2 for '{{node Equal}} = Equal[T=DT_INT64, incompatible_shape_error=true](ArgMax, ArgMax_1)' with input shapes: [?,17], [?,2].
The error is clear, the model output a dimension 2 and my output has dimension 17, although I understand the issue, I can't find a way of fixing it, any ideas?
I think your output shape is not "output[0].shape = (17, 185)" but "dense (Dense) (None, 2, 185) ".
You need to change your output shape or change your layer structure.
LSTM output is a list of encoder_outputs, when you specify return_sequences=True. hence; I suggest just using the last item of encoder_outputs as the input of your Dense layer. you can see the example section of this link to the documentation. It may help you.
I am currently working on a question answering system. I create a synthetic dataset that contains multiple words in the answers. But, the answers are not a span of the given context.
Initially, I am planning to test it using a deep learning-based model. But I have some problems building the model.
This is how I vectorized data.
def vectorize(data, word2idx, story_maxlen, question_maxlen, answer_maxlen):
""" Create the story and question vectors and the label """
Xs, Xq, Y = [], [], []
for story, question, answer in data:
xs = [word2idx[word] for word in story]
xq = [word2idx[word] for word in question]
y = [word2idx[word] for word in answer]
#y = np.zeros(len(word2idx) + 1)
#y[word2idx[answer]] = 1
Xs.append(xs)
Xq.append(xq)
Y.append(y)
return (pad_sequences(Xs, maxlen=story_maxlen),
pad_sequences(Xq, maxlen=question_maxlen),
pad_sequences(Y, maxlen=answer_maxlen))
#np.array(Y))
below is how I create the model.
# story encoder. Output dim: (None, story_maxlen, EMBED_HIDDEN_SIZE)
story_encoder = Sequential()
story_encoder.add(Embedding(input_dim=vocab_size,
output_dim=EMBED_HIDDEN_SIZE,
input_length=story_maxlen))
story_encoder.add(Dropout(0.3))
# question encoder. Output dim: (None, question_maxlen, EMBED_HIDDEN_SIZE)
question_encoder = Sequential()
question_encoder.add(Embedding(input_dim=vocab_size,
output_dim=EMBED_HIDDEN_SIZE,
input_length=question_maxlen))
question_encoder.add(Dropout(0.3))
# episodic memory (facts): story * question
# Output dim: (None, question_maxlen, story_maxlen)
facts_encoder = Sequential()
facts_encoder.add(Merge([story_encoder, question_encoder],
mode="dot", dot_axes=[2, 2]))
facts_encoder.add(Permute((2, 1)))
## combine response and question vectors and do logistic regression
answer = Sequential()
answer.add(Merge([facts_encoder, question_encoder],
mode="concat", concat_axis=-1))
answer.add(LSTM(LSTM_OUTPUT_SIZE, return_sequences=True))
answer.add(Dropout(0.3))
answer.add(Flatten())
answer.add(Dense(vocab_size,activation= "softmax"))
answer.compile(optimizer="rmsprop", loss="categorical_crossentropy",
metrics=["accuracy"])
answer.fit([Xs_train, Xq_train], Y_train,
batch_size=BATCH_SIZE, nb_epoch=NBR_EPOCHS,
validation_data=([Xs_test, Xq_test], Y_test))
and this is the summary of the model
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
merge_46 (Merge) (None, 5, 616) 0
_________________________________________________________________
lstm_23 (LSTM) (None, 5, 32) 83072
_________________________________________________________________
dropout_69 (Dropout) (None, 5, 32) 0
_________________________________________________________________
flatten_9 (Flatten) (None, 160) 0
_________________________________________________________________
dense_22 (Dense) (None, 37) 5957
=================================================================
Total params: 93,765.0
Trainable params: 93,765.0
Non-trainable params: 0.0
_________________________________________________________________
It gives the following error.
ValueError: Error when checking model target: expected dense_22 to have shape (None, 37) but got array with shape (1000, 2)
I think the error is related to Y_train, Y_test. I should encode them to categorical values and the answers are not spans of text, but sequential. I don't know what/how to do it.
how can I fix it? any ideas?
EDIT:
When I use sparse_categorical_crossentropy in the loss, and Reshape(2,-1);
answer.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
merge_94 (Merge) (None, 5, 616) 0
_________________________________________________________________
lstm_65 (LSTM) (None, 5, 32) 83072
_________________________________________________________________
dropout_139 (Dropout) (None, 5, 32) 0
_________________________________________________________________
reshape_22 (Reshape) (None, 2, 80) 0
_________________________________________________________________
dense_44 (Dense) (None, 2, 37) 2997
=================================================================
Total params: 90,805.0
Trainable params: 90,805.0
Non-trainable params: 0.0
_________________________________________________________________
EDIT2:
The model after modifications
# story encoder. Output dim: (None, story_maxlen, EMBED_HIDDEN_SIZE)
story_encoder = Sequential()
story_encoder.add(Embedding(input_dim=vocab_size,
output_dim=EMBED_HIDDEN_SIZE,
input_length=story_maxlen))
story_encoder.add(Dropout(0.3))
# question encoder. Output dim: (None, question_maxlen, EMBED_HIDDEN_SIZE)
question_encoder = Sequential()
question_encoder.add(Embedding(input_dim=vocab_size,
output_dim=EMBED_HIDDEN_SIZE,
input_length=question_maxlen))
question_encoder.add(Dropout(0.3))
# episodic memory (facts): story * question
# Output dim: (None, question_maxlen, story_maxlen)
facts_encoder = Sequential()
facts_encoder.add(Merge([story_encoder, question_encoder],
mode="dot", dot_axes=[2, 2]))
facts_encoder.add(Permute((2, 1)))
## combine response and question vectors and do logistic regression
## combine response and question vectors and do logistic regression
answer = Sequential()
answer.add(Merge([facts_encoder, question_encoder],
mode="concat", concat_axis=-1))
answer.add(LSTM(LSTM_OUTPUT_SIZE, return_sequences=True))
answer.add(Dropout(0.3))
#answer.add(Flatten())
answer.add(keras.layers.Reshape((2, -1)))
answer.add(Dense(vocab_size,activation= "softmax"))
answer.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
answer.fit([Xs_train, Xq_train], Y_train,
batch_size=BATCH_SIZE, nb_epoch=NBR_EPOCHS,
validation_data=([Xs_test, Xq_test], Y_test))
It still gives
ValueError: Error when checking model target: expected dense_46 to have 3 dimensions, but got array with shape (1000, 2)
As far as I understand - Y_train, Y_test comprise of indexes (not one-hot vectors). If so - change loss to sparse_categorical_entropy:
answer.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
As far as I understand - Y_train, Y_test have a sequence dimension. And the length of questions (5) doesn't equal to the length of the answers (2). This dimension is removed by Flatten(). Try to replace Flatten() by Reshape():
# answer.add(Flatten())
answer.add(tf.keras.layers.Reshape((2, -1)))
I am new to Keras and deep learning and was working with MNIST on Keras. When I created a model using
model = models.Sequential()
model.add(layers.Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(layers.Dense(32,activation ='relu'))
model.add(layers.Dense(10,activation='softmax'))
and then I printed it
print(model)
output is
<keras.engine.sequential.Sequential at 0x7f3d554f6710>
My question is that is there any way to see a better result of Keras, meaning if i print model i can see that i have 3 hidden layers with first hidden layer having 512 hidden units and 784 input units, 2nd hidden layer having 512 input units and 32 hidden units and so on.
You can also try plot_model()
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(tf.keras.layers.Dense(32,activation ='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax'))
model.summary()
from keras.utils.vis_utils import plot_model
plot_model(model, show_shapes=True, show_layer_names=True)
model.summary() will print he entire model for you.
model = Sequential()
model.add(Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(Dense(32,activation ='relu'))
model.add(Dense(10,activation='softmax'))
model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 512) 401920
_________________________________________________________________
dense_1 (Dense) (None, 32) 16416
_________________________________________________________________
dense_2 (Dense) (None, 10) 330
=================================================================
Total params: 418,666
Trainable params: 418,666
Non-trainable params: 0
____________________________
Research done before asking this question:
Error when checking target: expected dense_2 to have shape (None, 256) but got array with shape (16210, 4096)
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (10000, 1)
Error when checking target: expected dense_3 to have shape (2,) but got array with shape (1,)
I have searched for a solution to this problem for days now. Please help me figure this out.
vocab_size = 5000
dim = 32
input_length_var = 500
model = Sequential()
model.add(Embedding(vocab_size, dim, input_length=input_length_var))
model.add(LSTM(100))
model.add(Dense(1, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
print(model.summary())
The above code is my model. I will now print the summary of above model:
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 500, 1) 500
_________________________________________________________________
lstm_1 (LSTM) (None, 100) 40800
_________________________________________________________________
dense_1 (Dense) (None, 1) 101
_________________________________________________________________
dense_2 (Dense) (None, 1) 2
=================================================================
Total params: 41,403
Trainable params: 41,403
Non-trainable params: 0
And finally I will show you the result of np.shape():
(1117228, 500)
(1117228, 500)
I have tried everything from Reshape() to adding input_shape to the dense layers but the result is always the same. What am I doing wrong and how to I fix this? My task is sentiment analysis.
EDIT: I was told that the dimensions of output needed to be (1117228,1) and I needed sentiment scores in train_test_split for the labels. The first half of my csv is negative sentiment and the other half is positive sentiment. How would I use this?