I implemented two models, one time using the sequential way and one time with the functional API. Now the both models give different results, which kind of makes no sense to me.
I cannot figure out, what the problem is. Any ideas or solutions?
Here both models:
Sequential Model:
model = Sequential()
embedding_layer = Embedding(VOCAB_SIZE +1, EMBEDDING_SIZE, mask_zero= True)
model.add(embedding_layer)
model.add(Bidirectional(LSTM(HIDDEN_SIZE, return_sequences= True))),
model.add(TimeDistributed(Dense(NUM_LABELS, activation='softmax')))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_sents_padded, train_labels_padded, batch_size=4, epochs=10,
validation_data=(dev_sents_padded, dev_labels_padded))
score, acc = model.evaluate(dev_sents_padded, dev_labels_padded)
print("\nAccuracy: ", acc)
Functional Model:
inputs = Input(shape=(MAX_LENGTH,))
embedding = Embedding(VOCAB_SIZE +1, EMBEDDING_SIZE, mask_zero= True)(inputs)
left = LSTM(HIDDEN_SIZE, return_sequences=True)(embedding)
right = LSTM(HIDDEN_SIZE, go_backwards=True, return_sequences=True)
(embedding)
left_right = concatenate([left, right])
left_right = TimeDistributed(Dense(NUM_LABELS, activation='softmax'))
(left_right)
combined_model = Model(inputs=inputs, outputs=left_right)
combined_model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
combined_model.fit(
train_sents_padded,
train_labels_padded,
batch_size=4,
epochs=10,
validation_data=(dev_sents_padded, dev_labels_padded)
)
score, acc = combined_model.evaluate(dev_sents_padded, dev_labels_padded)
print("\nBidirectional LSTM Accuracy: ", acc)
+++
Summaries:
Sequential model:
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, None, 50) 26150
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 100) 40400
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 61) 6161
=================================================================
Total params: 72,711
Trainable params: 72,711
Non-trainable params: 0 `
Functional model:
Layer (type) Output Shape Param # Connected to
========================================================================
input_1 (InputLayer) (None, 34) 0
____________________________________________________________________
embedding_2 (Embedding) (None, 34, 50) 26150 input_1[0][0]
______________________________________________________________________
lstm_2 (LSTM) (None, 34, 50) 20200 embedding_2[0][0]
____________________________________________________________________
lstm_3 (LSTM) (None, 34, 50) 20200 embedding_2[0][0]
_________________________________________________________________
concatenate_1 (Concatenate)(None, 34, 100) 0 lstm_2[0][0]
lstm_3[0][0]
___________________________________________________________
time_distributed_2 (TimeDistrib (None, 34, 61) 6161 concatenate_1[0][0]
=====================================================================
Total params: 72,711
Trainable params: 72,711
Non-trainable params: 0`
+++
If I change VOCAB_SIZE + 1 to VOCAB_SIZE in the seqential model the acc is 59, but only on every third run??
Related
I am working on customizing a layer to use in my model.
The core part is the "call" function as,
class Custom_Layer(Layer):
// some code
def call(self, inputs, **kwargs):
kernel = mul(self.base, self.diag_start - self.diag_end)
outputs = matmul(a=inputs, b=kernel)
if self.use_bias:
outputs = tf.nn.bias_add(outputs, self.bias)
if self.activation is not None:
outputs = self.activation(outputs)
return outputs
// some code
and it is used in a simple model.
inputs = tf.keras.layers.Input(shape=(784,),dtype='float32')
layer1 = Custom_layer(2000, **Custom_layer_config, activation='tanh')(inputs)
layer2 = Custom_layer(200, **Custom_layer_config, activation='tanh')(layer1)
output_lay = Custom_layer(10, **Custom_layer_config, activation='softmax')(layer2)
model = tf.keras.models.Model(inputs=inputs, outputs=output_lay)
opt = tf.keras.optimizers.Adamax(learning_rate=0.02)
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
It is supposed to print like this:
Model: "functional_13"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_8 (InputLayer) [(None, 784)] 0
_________________________________________________________________
CustomLayer_18 (Custom_Layer) (None, 2000) 1570784
_________________________________________________________________
CustomLayer_19 (Custom_Layer) (None, 200) 402200
_________________________________________________________________
CustomLayer_20 (Custom_Layer) (None, 10) 2210
=================================================================
Total params: 1,975,194
Trainable params: 5,194
Non-trainable params: 1,970,000
_________________________________________________________________
But prints this:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 784)] 0
tf.linalg.matmul_3 (TFOpLam (None, 2000) 0
bda)
tf.math.tanh_2 (TFOpLambda) (None, 2000) 0
tf.linalg.matmul_4 (TFOpLam (None, 200) 0
bda)
tf.math.tanh_3 (TFOpLambda) (None, 200) 0
tf.linalg.matmul_5 (TFOpLam (None, 10) 0
bda)
tf.compat.v1.nn.softmax_1 ( (None, 10) 0
TFOpLambda)
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
The first summary is what I got from author's repository and the second summary is from my run of the same code without changing anything..
The code is not a complex one but it is weird why there is no parameters at all.
My question is that what is wrong here.
Try to make it as an inherited class from this example.
Sample: Custom LSTM class
import tensorflow as tf
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Definition
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MyLSTMLayer( tf.keras.layers.LSTM ):
def __init__(self, units, return_sequences, return_state):
super(MyLSTMLayer, self).__init__( units, return_sequences=True, return_state=False )
self.num_units = units
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_units])
def call(self, inputs):
return tf.matmul(inputs, self.kernel)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
start = 3
limit = 12
delta = 3
sample = tf.range( start, limit, delta )
sample = tf.cast( sample, dtype=tf.float32 )
sample = tf.constant( sample, shape=( 1, 1, 3 ) )
layer = MyLSTMLayer( 3, True, False )
model = tf.keras.Sequential([
tf.keras.Input(shape=(1, 3)),
layer,
])
model.summary()
print( sample )
print( model.predict(sample) )
Output:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
my_lstm_layer (MyLSTMLayer) (None, 1, 3) 9
=================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
_________________________________________________________________
tf.Tensor([[[3. 6. 9.]]], shape=(1, 1, 3), dtype=float32)
1/1 [==============================] - 1s 575ms/step
[[[-2.8894916 -2.146874 13.688236 ]]]
I wrote the following model fn:
from tensorflow.keras.layers import Dense, LSTM, Dropout, Input, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import tensorflow_addons as tfa
import tensorflow as tf
def get_model(num_features, output_size, output_bias=None):
output_bias = tf.keras.initializers.Constant(output_bias)
opt = Adam(learning_rate=0.0008)
inputs = Input(shape=[None, num_features], dtype=tf.float32, ragged=True)
layers = LSTM(32, activation='tanh')(
inputs.to_tensor(), mask=tf.sequence_mask(inputs.row_lengths()))
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(32, activation='relu')(layers)
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(32, activation='relu')(layers)
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(output_size, activation='sigmoid',
bias_initializer=output_bias)(layers)
model = Model(inputs, layers)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=opt, metrics=[tfa.metrics.F1Score(num_classes=2)])
model.summary()
return model
here is the model summary:
Model: "model_5"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_6 (InputLayer) [(None, None, 11)] 0
__________________________________________________________________________________________________
input.row_lengths_5 (InstanceMe (None,) 0 input_6[0][0]
__________________________________________________________________________________________________
input.to_tensor_5 (InstanceMeth (None, None, 11) 0 input_6[0][0]
__________________________________________________________________________________________________
tf.sequence_mask_5 (TFOpLambda) (None, None) 0 input.row_lengths_5[0][0]
__________________________________________________________________________________________________
lstm_5 (LSTM) (None, 32) 5632 input.to_tensor_5[0][0]
tf.sequence_mask_5[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 32) 128 lstm_5[0][0]
__________________________________________________________________________________________________
dropout_15 (Dropout) (None, 32) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
dense_15 (Dense) (None, 32) 1056 dropout_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 32) 128 dense_15[0][0]
__________________________________________________________________________________________________
dropout_16 (Dropout) (None, 32) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
dense_16 (Dense) (None, 32) 1056 dropout_16[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 32) 128 dense_16[0][0]
__________________________________________________________________________________________________
dropout_17 (Dropout) (None, 32) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 1) 33 dropout_17[0][0]
==================================================================================================
Total params: 8,161
Trainable params: 7,969
Non-trainable params: 192
__________________________________________________________________________________________________
And here are the shapes of my data:
print(train_x.shape,train_y.shape)
print(val_x.shape,val_y.shape)
(52499, None, 11) (52499,)
(17500, None, 11) (17500,)
When trying to fit my model, I get the following error:
model.fit(train_x, train_y, epochs=300, batch_size=500, validation_data=(val_x, val_y))
ValueError: Dimension 0 in both shapes must be equal, but are 2 and 1. Shapes are [2] and [1].
I can't understand what is wrong with the shapes.
Your model seems fine. The problem is that you are running into an open issue with the tfa.metrics.F1Score. For your binary case, you will have to change the parameters of the F1Score to tfa.metrics.F1Score(num_classes=1, threshold=0.5). Here is a complete working example:
from tensorflow.keras.layers import Dense, LSTM, Dropout, Input, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import tensorflow_addons as tfa
import tensorflow as tf
def get_model(num_features, output_size, output_bias=0.001):
output_bias = tf.keras.initializers.Constant(output_bias)
opt = Adam(learning_rate=0.0008)
inputs = Input(shape=[None, num_features], dtype=tf.float32, ragged=True)
layers = LSTM(32, activation='tanh')(
inputs.to_tensor(), mask=tf.sequence_mask(inputs.row_lengths()))
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(32, activation='relu')(layers)
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(32, activation='relu')(layers)
layers = BatchNormalization()(layers)
layers = Dropout(0.05)(layers)
layers = Dense(output_size, activation='sigmoid',
bias_initializer=output_bias)(layers)
model = Model(inputs, layers)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=opt, metrics=[tfa.metrics.F1Score(num_classes=1, threshold=0.5)])
model.summary()
return model
model = get_model(11, 1)
rt = tf.RaggedTensor.from_row_splits(values=tf.ones([5, 11], tf.int32),
row_splits=[0, 2, 5])
model.fit(rt, tf.random.uniform((2,1), maxval=2), epochs=300, batch_size=2, verbose=2)
Alternatively, you just define your own F1Score method and set it as metric in your model. See this post for more information.
I'm trying to build an undercomplete autoencoder for music dimensionality reduction. My Autoencoder class is modular, I can give in input a list of convlayers sizes and it creates me automatically the model. The problem is that when I try to create a model with more than 2 convolutional layers the decoder returns me the wrong dimensions. Here there is my AE model:
class Autoencoder(K.Model):
"""
Modular Autoencoder class
"""
def __init__(self,
in_shape: Tuple[int,int,int],
num_hidden_layers: int,
activation_func: str = "relu",
hidden_activation: str = "sigmoid",
conv_layers_sizes: List[int] = [128, 64]):
super(Autoencoder, self).__init__()
# Structure of the model
self.in_shape = in_shape
self.latent_dim = num_hidden_layers
self.conv_layers_sizes = conv_layers_sizes
self.hidden_activation = hidden_activation
# hyperparameters of the model
self.activation_func = activation_func
# build encoder and decoder
self.encoder = self._build_encoder()
self.decoder = self._build_decoder()
#self.last_cLayer_size = None
def summary(self):
"""
Function used to show the summary of the Autoencoder
"""
self.encoder.summary()
self.decoder.summary()
def _create_conv_layer(self,
n_filters: int,
enc_model) -> None:
"""
_create_conv_layer add a Conv1D layer to a given model
"""
enc_model.add(layers.Conv1D(n_filters,
kernel_size=3,
activation=self.activation_func,
padding='same',
strides=2,
name=f"Conv{n_filters}"))
enc_model.add(layers.BatchNormalization())
# try to not use max pooling
#enc_model.add(layers.MaxPool1D(2, strides=1,
# padding="same",
# name=f"BtchNorm_{n_filters}"))
def _create_deconv_layer(self,
n_filters: int,
decon_model) -> None:
"""
_create_deconv_layer add a Conv1DTranspose layer to a given model
"""
decon_model.add(layers.Conv1DTranspose(
n_filters,
kernel_size=3,
strides=2,
activation=self.activation_func,
padding='same',
name=f"ConvTransp{n_filters}"))
def _build_encoder(self) -> K.Sequential:
"""
_build_encoder creates the encoder
"""
model_encoder = K.Sequential(name="encoder")
model_encoder.add(layers.InputLayer(input_shape=self.in_shape))
# create encoder
for layer_size in self.conv_layers_sizes:
self._create_conv_layer(n_filters=layer_size,
enc_model=model_encoder)
# get the dimensions of the last layer in order to paste it into the
# decoder
last_layer = model_encoder.layers[-2]
_, *self.last_cLayer_size = last_layer.output_shape
#model_encoder.add(layers.GlobalAveragePooling2D(name="Flatten"))
model_encoder.add(layers.Flatten(name="Flatten"))
# hidden layers
model_encoder.add(layers.Dense(self.latent_dim,
activation=self.hidden_activation,
name=f"hidden_unit_{self.latent_dim}"))
return model_encoder
def _build_decoder(self) -> K.Sequential:
"""
_build_decoder creates the decoder
"""
model_decoder = K.Sequential(name="decoder")
model_decoder.add(layers.InputLayer(input_shape=self.latent_dim,
name="decoder_input"))
# calculate the dimension of the dense layer after the hidden
# representations
dense_to_reshape = 1
for dim in self.last_cLayer_size:
dense_to_reshape *= dim
# create a trainable dense layer with dimension adeguate to be reshaped
# in the same shape of the last layer of the encoder
model_decoder.add(layers.Dense(dense_to_reshape,
activation=self.activation_func))
model_decoder.add(layers.Dropout(0.8))
model_decoder.add(layers.Reshape(self.last_cLayer_size))
# create deconvolutions
for layer_size in reversed(self.conv_layers_sizes):
self._create_deconv_layer(layer_size, model_decoder)
model_decoder.add(layers.Conv1D(self.in_shape[-1],
kernel_size=3,
activation='sigmoid',
padding='same',
name=f"last_layer"))
return model_decoder
def call(self, x):
"""
Adapts the call method to the Autoencoder.
In this case call just reapplies all ops in the graph to the new inputs
(e.g. build a new computational graph from the provided inputs).
"""
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
here there are two examples of the output:
AE_model = Autoencoder(in_shape=(20, 862),
num_hidden_layers=2,
conv_layers_sizes=[128, 64],
activation_func="relu",
hidden_activation="sigmoid")
AE_model.summary()
OUT:
Model: "encoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Conv128 (Conv1D) (None, 10, 128) 331136
_________________________________________________________________
batch_normalization_10 (Batc (None, 10, 128) 512
_________________________________________________________________
Conv64 (Conv1D) (None, 5, 64) 24640
_________________________________________________________________
batch_normalization_11 (Batc (None, 5, 64) 256
_________________________________________________________________
Flatten (Flatten) (None, 320) 0
_________________________________________________________________
hidden_unit_2 (Dense) (None, 2) 642
=================================================================
Total params: 357,186
Trainable params: 356,802
Non-trainable params: 384
_________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_5 (Dense) (None, 320) 960
_________________________________________________________________
dropout_5 (Dropout) (None, 320) 0
_________________________________________________________________
reshape_5 (Reshape) (None, 5, 64) 0
_________________________________________________________________
ConvTransp64 (Conv1DTranspos (None, 10, 64) 12352
_________________________________________________________________
ConvTransp128 (Conv1DTranspo (None, 20, 128) 24704
_________________________________________________________________
last_layer (Conv1D) (None, 20, 862) 331870
=================================================================
AE_model = Autoencoder(in_shape=(20, 862),
num_hidden_layers=2,
conv_layers_sizes=[128, 64, 32],
activation_func="relu",
hidden_activation="sigmoid")
AE_model.summary()
OUT:
Model: "encoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Conv128 (Conv1D) (None, 10, 128) 331136
_________________________________________________________________
batch_normalization_12 (Batc (None, 10, 128) 512
_________________________________________________________________
Conv64 (Conv1D) (None, 5, 64) 24640
_________________________________________________________________
batch_normalization_13 (Batc (None, 5, 64) 256
_________________________________________________________________
Conv32 (Conv1D) (None, 3, 32) 6176
_________________________________________________________________
batch_normalization_14 (Batc (None, 3, 32) 128
_________________________________________________________________
Flatten (Flatten) (None, 96) 0
_________________________________________________________________
hidden_unit_2 (Dense) (None, 2) 194
=================================================================
Total params: 363,042
Trainable params: 362,594
Non-trainable params: 448
_________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_6 (Dense) (None, 96) 288
_________________________________________________________________
dropout_6 (Dropout) (None, 96) 0
_________________________________________________________________
reshape_6 (Reshape) (None, 3, 32) 0
_________________________________________________________________
ConvTransp32 (Conv1DTranspos (None, 6, 32) 3104
_________________________________________________________________
ConvTransp64 (Conv1DTranspos (None, 12, 64) 6208
_________________________________________________________________
ConvTransp128 (Conv1DTranspo (None, 24, 128) 24704
_________________________________________________________________
last_layer (Conv1D) (None, 24, 862) 331870
=================================================================
Why having more than 2 layers messes up with my final dimensions?
I ended up with a solution on my own. The problem is the stride when the stride is > 1. Setting the stride to be always 1 makes Everything works. An explanation can be found here
I'm trying to train DC-CNN model for text classification on a given dataset.
What am I doing wrong here?
Code for Model:
def define_model(length, vocab_size):
# channel 1
inputs1 = Input(shape=(length,))
embedding1 = Embedding(vocab_size, 100)(inputs1)
conv1 = Conv1D(filters=32, kernel_size=4, activation='relu')(embedding1)
drop1 = Dropout(0.5)(conv1)
pool1 = MaxPooling1D(pool_size=1)(drop1)
flat1 = Flatten()(pool1)
# channel 2
inputs2 = Input(shape=(length,))
embedding2 = Embedding(vocab_size, 100)(inputs2)
conv2 = Conv1D(filters=32, kernel_size=6, activation='relu')(embedding2)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling1D(pool_size=1)(drop2)
flat2 = Flatten()(pool2)
merged = concatenate([flat1, flat2])
# interpretation
dense1 = Dense(10, activation='relu')(merged)
outputs = Dense(1, activation='sigmoid')(dense1)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
# compile
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# summarize
print(model.summary())
return model
model = define_model(length, vocab_size)
model.fit([trainX], array(trainLabels), epochs=10, batch_size=16)
I am getting this error:
AssertionError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:517 _run_internal_graph
assert x_id in tensor_dict, 'Could not compute output ' + str(x)
AssertionError: Could not compute output Tensor("dense_17/Sigmoid:0", shape=(None, 1), dtype=float32)
I have tried to reshape the inputs "trainX" and "trainLabels" by using this code but I got the same error
trainX=np.reshape(trainX,(40, 50))
trainLabels=np.reshape(trainLabels,(40, 1))
This is the summary of the model :
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_17 (InputLayer) [(None, 20)] 0
__________________________________________________________________________________________________
input_18 (InputLayer) [(None, 20)] 0
__________________________________________________________________________________________________
embedding_16 (Embedding) (None, 20, 100) 541100 input_17[0][0]
__________________________________________________________________________________________________
embedding_17 (Embedding) (None, 20, 100) 541100 input_18[0][0]
__________________________________________________________________________________________________
conv1d_16 (Conv1D) (None, 17, 32) 12832 embedding_16[0][0]
__________________________________________________________________________________________________
conv1d_17 (Conv1D) (None, 15, 32) 19232 embedding_17[0][0]
__________________________________________________________________________________________________
dropout_16 (Dropout) (None, 17, 32) 0 conv1d_16[0][0]
__________________________________________________________________________________________________
dropout_17 (Dropout) (None, 15, 32) 0 conv1d_17[0][0]
__________________________________________________________________________________________________
max_pooling1d_16 (MaxPooling1D) (None, 17, 32) 0 dropout_16[0][0]
__________________________________________________________________________________________________
max_pooling1d_17 (MaxPooling1D) (None, 15, 32) 0 dropout_17[0][0]
__________________________________________________________________________________________________
flatten_16 (Flatten) (None, 544) 0 max_pooling1d_16[0][0]
__________________________________________________________________________________________________
flatten_17 (Flatten) (None, 480) 0 max_pooling1d_17[0][0]
__________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 1024) 0 flatten_16[0][0]
flatten_17[0][0]
__________________________________________________________________________________________________
dense_16 (Dense) (None, 10) 10250 concatenate_8[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 1) 11 dense_16[0][0]
==================================================================================================
Total params: 1,124,525
Trainable params: 1,124,525
Non-trainable params: 0
How can I fix this error Please?
since you have 2 inputs in keras model, so you have to split your trainX in to 2 different arrays, or a tuple of 2 arrays. you cannot give single array as input.
I have a subclass Model of tf.keras.Model,code is following
import tensorflow as tf
class Mymodel(tf.keras.Model):
def __init__(self, classes, backbone_model, *args, **kwargs):
super(Mymodel, self).__init__(self, args, kwargs)
self.backbone = backbone_model
self.classify_layer = tf.keras.layers.Dense(classes,activation='sigmoid')
def call(self, inputs):
x = self.backbone(inputs)
x = self.classify_layer(x)
return x
inputs = tf.keras.Input(shape=(224, 224, 3))
model = Mymodel(inputs=inputs, classes=61,
backbone_model=tf.keras.applications.MobileNet())
model.build(input_shape=(20, 224, 224, 3))
model.summary()
the result is :
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenet_1.00_224 (Model) (None, 1000) 4253864
_________________________________________________________________
dense (Dense) multiple 61061
=================================================================
Total params: 4,314,925
Trainable params: 4,293,037
Non-trainable params: 21,888
_________________________________________________________________
but I want to see the all layers of mobilenet,then I tried to extract all layers of mobilenet and put in the model:
import tensorflow as tf
class Mymodel(tf.keras.Model):
def __init__(self, classes, backbone_model, *args, **kwargs):
super(Mymodel, self).__init__(self, args, kwargs)
self.backbone = backbone_model
self.classify_layer = tf.keras.layers.Dense(classes,activation='sigmoid')
def my_process_layers(self,inputs):
layers = self.backbone.layers
tmp_x = inputs
for i in range(1,len(layers)):
tmp_x = layers[i](tmp_x)
return tmp_x
def call(self, inputs):
x = self.my_process_layers(inputs)
x = self.classify_layer(x)
return x
inputs = tf.keras.Input(shape=(224, 224, 3))
model = Mymodel(inputs=inputs, classes=61,
backbone_model=tf.keras.applications.MobileNet())
model.build(input_shape=(20, 224, 224, 3))
model.summary()
then the resule not changed.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenet_1.00_224 (Model) (None, 1000) 4253864
_________________________________________________________________
dense (Dense) multiple 61061
=================================================================
Total params: 4,314,925
Trainable params: 4,293,037
Non-trainable params: 21,888
_________________________________________________________________
then I tried to extract one layer insert to the model :
import tensorflow as tf
class Mymodel(tf.keras.Model):
def __init__(self, classes, backbone_model, *args, **kwargs):
super(Mymodel, self).__init__(self, args, kwargs)
self.backbone = backbone_model
self.classify_layer = tf.keras.layers.Dense(classes,activation='sigmoid')
def call(self, inputs):
x = self.backbone.layers[1](inputs)
x = self.classify_layer(x)
return x
inputs = tf.keras.Input(shape=(224, 224, 3))
model = Mymodel(inputs=inputs, classes=61,
backbone_model=tf.keras.applications.MobileNet())
model.build(input_shape=(20, 224, 224, 3))
model.summary()
It did not change either.I am so confused.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenet_1.00_224 (Model) (None, 1000) 4253864
_________________________________________________________________
dense (Dense) multiple 244
=================================================================
Total params: 4,254,108
Trainable params: 4,232,220
Non-trainable params: 21,888
_________________________________________________________________
but I find that the parameter of dense layer changed,I dont know what happend.
#Ioannis 's answer is perfectly fine, but unfortunately it drops the keras 'Model Subclassing' structure that is present in the question. If, just like me, you want to keep this model subclassing and still show all layers in the summary, you can branch down into all the individual layers of the more complex model using a for loop:
class MyMobileNet(tf.keras.Sequential):
def __init__(self, input_shape=(224, 224, 3), classes=61):
super(MyMobileNet, self).__init__()
self.backbone_model = [layer for layer in
tf.keras.applications.MobileNet(input_shape, include_top=False, pooling='avg').layers]
self.classificator = tf.keras.layers.Dense(classes,activation='sigmoid', name='classificator')
def call(self, inputs):
x = inputs
for layer in self.backbone_model:
x = layer(x)
x = self.classificator(x)
return x
model = MyMobileNet()
After this we can directly build the model and call the summary:
model.build(input_shape=(None, 224, 224, 3))
model.summary()
>
Model: "my_mobile_net"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0
_________________________________________________________________
conv1 (Conv2D) (None, 112, 112, 32) 864
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32) 128
_________________________________________________________________
....
....
conv_pw_13 (Conv2D) (None, 7, 7, 1024) 1048576
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024) 4096
_________________________________________________________________
conv_pw_13_relu (ReLU) (None, 7, 7, 1024) 0
_________________________________________________________________
global_average_pooling2d_13 (None, 1024) 0
_________________________________________________________________
classificator (Dense) multiple 62525
=================================================================
Total params: 3,291,389
Trainable params: 3,269,501
Non-trainable params: 21,888
_________________________________________________________________
In order to be able to view backbone's layers, you' ll have to construct your new model using backbone.input and backbone.output
from tensorflow.keras.models import Model
def Mymodel(backbone_model, classes):
backbone = backbone_model
x = backbone.output
x = tf.keras.layers.Dense(classes,activation='sigmoid')(x)
model = Model(inputs=backbone.input, outputs=x)
return model
input_shape = (224, 224, 3)
model = Mymodel(backbone_model=tf.keras.applications.MobileNet(input_shape=input_shape, include_top=False, pooling='avg'),
classes=61)
model.summary()
There is an argument expand_nested in the Method summary.
model.summary(expand_nested=True)
for layer in model.layers:
layer.summary()