I have a model trained.
summary is as follows
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 256) 2560
dense_1 (Dense) (None, 128) 32896
dropout (Dropout) (None, 128) 0
dense_2 (Dense) (None, 1) 129
=================================================================
Total params: 35,585
Trainable params: 35,585
Non-trainable params: 0
_________________________________________________________________
And have weights
for i,weight in enumerate(Model.weights):
exec('w{}=np.array(weight)'.format(i))
have test data for predict
x=test_data.iloc[0]
then I predict with model
Model.predict(np.array(x).reshape(1,9))
get array([[226241.66]], dtype=float32)
then I predict with weights
((x#w0+w1)#w2+w3)#w4+w5
get array([98039.99664026])
Can someone explain how the weights in model works?
And how to get the model-predict result with weights?
Try Model.layers which will return a list of all layers in your model, each layer has a function get_weights() which will return the weights as numpy arrays. I was able to reproduce the output of a simple 3 layer feed-forward model with this approach.
for i,layer in enumerate(model.layers):
exec('w{}=np.array(layer.get_weights()[0])'.format(i)) # weight
exec('b{}=np.array(layer.get_weights()[1])'.format(i)) # bias
X = np.random.randn(1,9)
np.allclose(((X#w1[0] + b1[1])#w2[0] + b2[1])#w4[0] + b4[1], model.predict(X)) # True
Note: In my examle layer 0 was a input layer (no weights) and layer 3 a dropout layer (no weights). When calling model.predict(), dropout is not applied, therefore you can ignore it in this case.
Related
This might be noob question. I have tried my best find the answers.
Basically I want LSTM to calculated error based on very timestep. I want to give true value for every timestep. I have tried giving dimension x=(2,10,1) and y=(2,10,1) which doesn't work , predict function outputs 3d array instead of 2d array. what I am doing wrong here?
I
You should use LSTM with return_sequences=True followed by Dense layer and then flatten the output of the Dense layer.
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
ins = Input(shape=(10, 3)) # considering 3 input features
lstm = LSTM(256, return_sequences=True)(ins)
dense = Dense(1)(lstm)
flat = Flatten()(dense)
model = Model(inputs=ins, outputs=flat)
model.summary()
This will build the following model
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 10, 3)] 0
_________________________________________________________________
lstm_1 (LSTM) (None, 10, 256) 266240
_________________________________________________________________
dense_1 (Dense) (None, 10, 1) 257
_________________________________________________________________
flatten (Flatten) (None, 10) 0
=================================================================
Total params: 266,497
Trainable params: 266,497
Non-trainable params: 0
_________________________________________________________________
model = tf.keras.Sequential([tf.keras.layers.Embedding(tokenizer.vocab_size, 64),tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,return_sequences=True))
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
The second layer has 64 hidden units and since the return_sequences=True, it will output 64 sequences as well. But how can it be fed to a 32 hidden units LSTM. Won't it cause shape mismatch error?
Actually no, it won't cause it. First of all the second layer won't have the output shape of 64, but instead of 128. This is because you are using Bidirectional layer, it will be concatenated by a forward and backward pass and so you output will be (None, None, 64+64=128). You can refer to the link.
The RNN data is shaped in the following was (Batch_size, time_steps, number_of_features). This means when you try to connect two layer with different neurons the features increased or decreased based on the number of neurons.You can follow the particular link for more details.
And for your particular code this is how the model summary will look like. So to answer in short their won't be a mismatch.
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 64) 32000
_________________________________________________________________
bidirectional (Bidirectional (None, None, 128) 66048
_________________________________________________________________
bidirectional_1 (Bidirection (None, 64) 41216
_________________________________________________________________
dense_2 (Dense) (None, 64) 4160
_________________________________________________________________
dense_3 (Dense) (None, 1) 65
=================================================================
Total params: 143,489
Trainable params: 143,489
Non-trainable params: 0
_________________________________________________________________
Currently, I am trying to understand quantization aware training in TensorFlow. I understand, that fake quantization nodes are required to gather dynamic range information as a calibration for the quantization operation. When I compare the same model once as "plain" Keras model and once as quantization aware model, the latter has more parameters, which makes sense since we need to store the minimum and maximum values for activations during the quantization aware training.
Consider the following example:
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Model
def get_model(in_shape):
inpt = layers.Input(shape=in_shape)
dense1 = layers.Dense(256, activation="relu")(inpt)
dense2 = layers.Dense(128, activation="relu")(dense1)
out = layers.Dense(10, activation="softmax")(dense2)
model = Model(inpt, out)
return model
The model has the following summary:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 784)] 0
_________________________________________________________________
dense_3 (Dense) (None, 256) 200960
_________________________________________________________________
dense_4 (Dense) (None, 128) 32896
_________________________________________________________________
dense_5 (Dense) (None, 10) 1290
=================================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
_________________________________________________________________
However, if i make my model optimization aware, it prints the following summary:
import tensorflow_model_optimization as tfmot
quantize_model = tfmot.quantization.keras.quantize_model
# q_aware stands for for quantization aware.
q_aware_model = quantize_model(standard)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 784)] 0
_________________________________________________________________
quantize_layer (QuantizeLaye (None, 784) 3
_________________________________________________________________
quant_dense_3 (QuantizeWrapp (None, 256) 200965
_________________________________________________________________
quant_dense_4 (QuantizeWrapp (None, 128) 32901
_________________________________________________________________
quant_dense_5 (QuantizeWrapp (None, 10) 1295
=================================================================
Total params: 235,164
Trainable params: 235,146
Non-trainable params: 18
_________________________________________________________________
I have two questions in particular:
What is the purpose of the quantize_layer with 3 parameters after the Input layer?
Why do we have 5 additional non-trainable parameters per layer and what are they used for exactly?
I appreciate any hint or further material that helps me (and others that stumble upon this question) understand quantization aware training.
The quantize layer is used to convert the float inputs to int8. The quantization parameters are used for output min/max and zero point calculations.
Quantized Dense Layers need a few additional parameters: min/max for kernel and min/max/zero-point for output activations.
For example:
BUFFER_SIZE = 10000
BATCH_SIZE = 64
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(train_dataset))
test_dataset = test_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(test_dataset))
def pad_to_size(vec, size):
zeros = [0] * (size - len(vec))
vec.extend(zeros)
return vec
...
model = tf.keras.Sequential([
tf.keras.layers.Embedding(encoder.vocab_size, 64),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=False)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
print(model.summary())
The print reads as:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 64) 523840
_________________________________________________________________
bidirectional (Bidirectional (None, 128) 66048
_________________________________________________________________
dense (Dense) (None, 64) 8256
_________________________________________________________________
dense_1 (Dense) (None, 1) 65
=================================================================
Total params: 598,209
Trainable params: 598,209
Non-trainable params: 0
I have the following question:
1) For the embedding layer, why is the ouput shape is (None, None, 64). I understand '64' is the vector length. Why are the other two None?
2) How is the output shape of bidirectional layer is (None, 128)? Why is it 128?
For the embedding layer, why is the ouput shape is (None, None, 64). I understand '64' is the vector length. Why are the other two None?
You can see this function produces (None,None) (including the batch dimension) (in other words it does input_shape=(None,) as default) if you don't define the input_shape to the first layer of the Sequential model.
If you pass in an input tensor of size (None, None) to an embedding layer, it produces a (None, None, 64) tensor assuming embedding dimension is 64. The first None is the batch dimension and the second is the time dimension (refers to the input_length parameter). So that's why you get a (None, None, 64) sized output.
How is the output shape of bidirectional layer is (None, 128)? Why is it 128?
Here, you have a Bidirectional LSTM. Your LSTM layer produces a (None, 64) sized output (when return_sequences=False). When you have a Bidirectional layer it is like having two LSTM layers (one going forward, other going backwards). And you have a default merge_mode of concat meaning that the two output states from forward and backward layers will be concatenated. This gives you a (None, 128) sized output.
I want to feed only RNN output at odd positions to the next RNN layer. How to achieve that in tensorflow?
I basically want to build the top layer in the following diagram, which halves the sequence size. The bottom layer is just a simple RNN.
Is this what you need?
from tensorflow.keras import layers, models
import tensorflow.keras.backend as K
inp = layers.Input(shape=(10, 5))
out = layers.LSTM(50, return_sequences=True)(inp)
out = layers.Lambda(lambda x: tf.stack(tf.unstack(out, axis=1)[::2], axis=1))(out)
out = layers.LSTM(50)(out)
out = layers.Dense(20)(out)
m = models.Model(inputs=inp, outputs=out)
m.summary()
You get the following model. You can see the second LSTM only gets 5 timesteps from the total 10 steps (i.e. every other output of the previous layer)
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 10, 5)] 0
_________________________________________________________________
lstm_2 (LSTM) (None, 10, 50) 11200
_________________________________________________________________
lambda_1 (Lambda) (None, 5, 50) 0
_________________________________________________________________
lstm_3 (LSTM) (None, 50) 20200
_________________________________________________________________
dense_1 (Dense) (None, 20) 1020
=================================================================
Total params: 32,420
Trainable params: 32,420
Non-trainable params: 0