I am trying to concatenate two sequential models. I have a model which is a concatenation of two sub-models, each of which is a concatenation of two sequential models. I have the following code but it doesn't work with Keras 2.3.0
model = Sequential()
sub_model1 = Sequential()
sub_model_channel1 = Sequential()
sub_model_channel2 = Sequential()
sub_model_channel1.add(Dropout(dropout_prob[0], input_shape=(channels, sequence_length,sequence_length)))
sub_model_channel2.add(Dropout(dropout_prob[0], input_shape=(channels, sequence_length,sequence_length)))
in1 = Input(shape=(channels, sequence_length,sequence_length))
in2 = Input(shape=(channels, sequence_length,sequence_length))
convs1 = model_unichannel(in1)
convs2 = model_unichannel(in2)
out1 = Concatenate()(convs1)
out2 = Concatenate()(convs2)
m1 = Model(inputs=in1, outputs=out1)
m2 = Model(inputs=in2, outputs=out2)
sub_model_channel1.add(m1)
sub_model_channel2.add(m2)
m = Concatenate()([sub_model_channel1, sub_model_channel2])
sub_model1.add(m)
model.add(sub_model1)
I am getting the following error
ValueError: Layer concatenate_3 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.engine.sequential.Sequential'>.
in the line m = Concatenate()([sub_model_channel1, sub_model_channel2]).
I have already looked at following solutions but nothing really solves my problem.
1) ValueError with Concatenate Layer (Keras functional API)
2) Merge 2 sequential models in Keras
I modified my code following the approach in the second link.
model = Sequential()
sub_model_channel1 = Sequential()
sub_model_channel2 = Sequential()
sub_model_channel1.add(Dropout(dropout_prob[0], input_shape=(channels, sequence_length,sequence_length)))
sub_model_channel2.add(Dropout(dropout_prob[0], input_shape=(channels, sequence_length,sequence_length)))
in1 = Input(shape=(channels, sequence_length,sequence_length))
in2 = Input(shape=(channels, sequence_length,sequence_length))
convs1 = model_unichannel(in1) #adds Conv, MaxPooling and Flatten layer
convs2 = model_unichannel(in2)
out1 = Concatenate()(convs1)
out2 = Concatenate()(convs2)
m1 = Model(inputs=in1, outputs=out1)
m2 = Model(inputs=in2, outputs=out2)
sub_model_channel1.add(m1)
sub_model_channel2.add(m2)
m = Concatenate()([sub_model_channel1.output, sub_model_channel2.output])
sub_model1 = Model([sub_model_channel1.input,sub_model_channel2.input], m)
model.add(sub_model1)
In this case I am getting an error ValueError: Layer model_3 expects 2 inputs, but it received 1 input tensors. Input received: [<tf.Tensor 'model_3_input:0' shape=(?, 7, 145, 145) dtype=float32>]. I understand this is because my model is also Sequential but how do I define the inputs? Also, is there any alternative way(apart from approach two) of doing this?
Related
I have a model that I load this way:
def YOLOv3_pretrained(n_classes=12, n_bbox=3):
yolo3 = tf.keras.models.load_model("yolov3/yolo3.h5")
yolo3.trainable = False
l3 = yolo3.get_layer('leaky_re_lu_71').output
l3_flat = tf.keras.layers.Flatten()(l3)
out3 = tf.keras.layers.Dense(100*(4+1+n_classes))(l3_flat)
out3 = Reshape((100, (4+1+n_classes)), input_shape=(12,))(out3)
yolo3 = Model(inputs=yolo3.input, outputs=[out3])
return yolo3
I want to add a Dense at the end of it but since it takes an input with shape (None, 416,416,3) it doesn't let me do it and it returns an error:
ValueError: The last dimension of the inputs to a Dense layer should be defined. Found None. Full input shape received: (None, None)
I also tried this way with a Sequential (I want to use just the last output of yolo):
def YOLOv3_Dense(n_classes=12):
yolo3 = tf.keras.models.load_model("yolov3/yolo3.h5")
model = Sequential()
model.add(yolo3)
model.add(Flatten())
model.add(Dense(100*(4+1+n_classes)))
model.add(Reshape((100, (4+1+n_classes)), input_shape=(413,413,3)))
return model
But it returns another error:
ValueError: All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.
Is there a way to add the final Dense layer?
The problem is that you are trying to reduce (flatten) an output with multiple None dimensions, which will not work if you want to use the output as input to another layer. You can try using a GlobalAveragePooling2D or GlobalMaxPooling2D instead:
import tensorflow as tf
yolo3 = tf.keras.models.load_model("yolo3.h5")
yolo3.trainable = False
l3 = yolo3.get_layer('leaky_re_lu_71').output
l3_flat = tf.keras.layers.GlobalMaxPooling2D()(l3)
out3 = tf.keras.layers.Dense(100*(4+1+12))(l3_flat)
out3 = tf.keras.layers.Reshape((100, (4+1+12)), input_shape=(12,))(out3)
yolo3 = tf.keras.Model(inputs=yolo3.input, outputs=[out3])
I'm trying to follow the tutorial given here.
This tutorial trains a Keras model using a genetic algorithm, with the PyGAD package. I'm interested in the binary classification case. My input matrix is of dimension 10000x20. Hence, I've created the following model using Keras:
input_layer = tensorflow.keras.layers.Input(20)
dense_layer1 = tensorflow.keras.layers.Dense(500, activation="relu")(input_layer)
dense_layer2 = tensorflow.keras.layers.Dense(500, activation="relu")(dense_layer1)
output_layer = tensorflow.keras.layers.Dense(1, activation="softmax")(dense_layer2)
model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)
keras_ga = pygad.kerasga.KerasGA(model=model,
num_solutions=10)
However, when I go to run the algorithm, using ga_instance.run(), I get the error:
ValueError: Shapes (10000,) and (10000, 1) are incompatible
I can't figure out why I'm getting this error? I want my Keras model to have 2 hidden layers, each with 500 hidden nodes and 1 output node.
I think the problem is related to how each output is represented in the array. if you have a single output for 10000 instances, then this is an example of preparing the data that works with PyGAD. Its shape is (1000, 1).
numpy.random.uniform(0, 1, (1000, 1))
Here is a code that works but for a simple network architecture because, based on the fitness function you used, the fitness sometimes is NaN.
As I do not have the same data you used, I generated the input/output data randomly.
import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,
weights_vector=solution)
model.set_weights(weights=model_weights_matrix)
predictions = model.predict(data_inputs)
cce = tensorflow.keras.losses.CategoricalCrossentropy()
solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001)
# print("solution_fitness", cce(data_outputs, predictions).numpy(), solution_fitness)
return solution_fitness
def callback_generation(ga_instance):
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1]))
data_inputs = numpy.random.uniform(0, 1, (1000, 20))
data_outputs = numpy.random.uniform(0, 1, (1000, 1))
# create model
from tensorflow.keras.layers import Dense, Dropout
l1_rate=1e-6
l2_rate = 1e-6
input_layer = tensorflow.keras.layers.InputLayer(20)
dense_layer1 = tensorflow.keras.layers.Dense(10, activation="relu",kernel_regularizer=tensorflow.keras.regularizers.l1_l2(l1=l1_rate, l2=l2_rate))
output_layer = tensorflow.keras.layers.Dense(1, activation="sigmoid")
model = tensorflow.keras.Sequential()
model.add(input_layer)
model.add(dense_layer1)
model.add(Dropout(0.2))
model.add(output_layer)
keras_ga = pygad.kerasga.KerasGA(model=model,
num_solutions=10)
# Run pygad
num_generations = 30
num_parents_mating = 5
initial_population = keras_ga.population_weights
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
initial_population=initial_population,
fitness_func=fitness_func,
on_generation=callback_generation)
ga_instance.run()
Thanks for using PyGAD!
I want to make a model like the below picture. (simplified)
So, practically, I want the weights with the same names to always have the same values during training. What I did was the code below:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
example_train_features = np.arange(12000).reshape(1000, 12)
example_lanbels = np.random.randint(2, size=1000) #these data are just for illustration purposes
train_ds = tf.data.Dataset.from_tensor_slices((example_train_features, example_lanbels)).shuffle(buffer_size = 1000).batch(32)
dense1 = layers.Dense(1, activation="relu") #input shape:4
dense2 = layers.Dense(2, activation="relu") #input shape:1
dense3 = layers.Dense(1, activation="sigmoid") #input shape:6
feature_input = keras.Input(shape=(12,), name="features")
nodes_list = []
for i in range(3):
first_lvl_input = feature_input[i :: 4] ######## marked line
out1 = dense1(first_lvl_input)
out2 = dense2(out1)
nodes_list.append(out2)
joined = layers.concatenate(nodes_list)
final_output = dense3(joined)
model = keras.Model(inputs = feature_input, outputs = final_output, name="extrema_model")
compile_and_fit(model, train_ds, val_ds, patience=4)
model.compile(loss = tf.keras.losses.BinaryCrossentropy(),
optimizer = tf.keras.optimizers.RMSprop(),
metrics=keras.metrics.BinaryAccuracy())
history = model.fit(train_ds, epochs=10, validation_data=val_ds)
But when I try to run this code I get this error:
MklConcatOp : Dimensions of inputs should match: shape[0][0]= 71 vs. shape[18][0] = 70
[[node extrema_model/concatenate_2/concat (defined at <ipython-input-373-5efb41d312df>:398) ]] [Op:__inference_train_function_15338]
(please don't pay attention to numbers as they are from my real code) this is because it gets the whole data including the labels as an input, but shouldn't Keras only feed the features itself? Anyway, if I write the marked line as below:
first_lvl_input = feature_input[i :12: 4]
it doesn't give me the above error anymore. But, then I get another error which I know why happens but I don't know how to resolve it.
InvalidArgumentError: Incompatible shapes: [4,1] vs. [32,1]
[[node gradient_tape/binary_crossentropy/logistic_loss/mul/BroadcastGradientArgs
(defined at <ipython-input-1-b82546367b3c>:398) ]] [Op:__inference_train_function_6098]
This is because keras is feeding again the whole batch array, whereas in Keras documentation it is written you shouldn't specify the batch dimension for the program as it understands itself, so I expected Keras to feed the data one by one for my code to work. So I appreciate any ideas on how to resolve this or on how to write a code for what I want. Thanks.
You can wrap the dense layers in timedistributed wrapper , and reshape your data to have three dimensions (1000,3,4)(batch, sequence, feature), so for each time step (=3 that replace your for loop code .) the four features will be multiplied with the same weights each time.
example_train_features = np.arange(12000).reshape(1000, 3, 4 )
example_lanbels = np.random.randint(2, size=1000) #these data are just for illustration purposes
train_ds = tf.data.Dataset.from_tensor_slices((example_train_features, example_lanbels)).shuffle(buffer_size = 1000).batch(32)
dense1 = layers.TimeDistributed(layers.Dense(1, activation="relu")) #input shape:4
dense2 =layers.TimeDistributed(layers.Dense(2, activation="relu")) #input shape:1
dense3 = layers.Dense(1, activation="sigmoid") #input shape:6
feature_input = keras.Input(shape=(3,4), name="features")
out1 = dense1(feature_input)
out2 = dense2(out1)
z = layers.Flatten()(out2)
final_output = dense3(z)
model = keras.Model(inputs = feature_input, outputs = final_output, name="extrema_model")
model.compile(loss = tf.keras.losses.BinaryCrossentropy(),
optimizer = tf.keras.optimizers.RMSprop(),
metrics=keras.metrics.BinaryAccuracy())
history = model.fit(train_ds, epochs=10)
I would like to merge two Keras model by using numpy.concatenate. I created two Keras model with output (None, 1). What I want to do is creating new layer with output (None, 2) by concatenating this two output. I tried below code but received "ValueError: zero-dimensional arrays cannot be concatenated"
def test():
model = Sequential()
model.add(Dense(1, input_shape=(1,)))
return model
a = test()
b= test()
x = np.concatenate([a.output, b.output], axis=1)
I am trying to implement number of networks using Keras applications. Here I am attaching a piece of code and this code works fine for ResNet50 and VGG16 but when it comes to MobileNet it generate the error:
ValueError: Error when checking target: expected dense_1 to have 4 dimensions, but got array with shape (24, 2)
I am working with 224x224 images with 3 channels and batch size of 24 and trying to classify them in 2 classes, so the number 24 mentioned in the error is the batch size but I am not sure about number 2, probably it is number of classes.
Btw is there anyone who knows why I am receiving this error for keras.applications.mobilenet?
# basic_model = ResNet50()
# basic_model = VGG16()
basic_model = MobileNet()
classes = list(iter(train_generator.class_indices))
basic_model.layers.pop()
for layer in basic_model.layers[:25]:
layer.trainable = False
last = basic_model.layers[-1].output
temp = Dense(len(classes), activation="softmax")(last)
fineTuned_model = Model(basic_model.input, temp)
fineTuned_model.classes = classes
fineTuned_model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
fineTuned_model.fit_generator(
train_generator,
steps_per_epoch=3764 // batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps=900 // batch_size)
fineTuned_model.save('mobile_model.h5')
From the source code, we can see that you're popping a Reshape() layer. Exactly the one that transforms the convolution's output (4D) into a class tensor (2D).
Source code:
if include_top:
if K.image_data_format() == 'channels_first':
shape = (int(1024 * alpha), 1, 1)
else:
shape = (1, 1, int(1024 * alpha))
x = GlobalAveragePooling2D()(x)
x = Reshape(shape, name='reshape_1')(x)
x = Dropout(dropout, name='dropout')(x)
x = Conv2D(classes, (1, 1),
padding='same', name='conv_preds')(x)
x = Activation('softmax', name='act_softmax')(x)
x = Reshape((classes,), name='reshape_2')(x)
But all the keras convolutional models are meant to be used in a different way. If you want your own number of classes, you should create these models with include_top=False. This way, the final part of the model (the classes part) will simply not exist and you just add your own layers:
basic_model = MobileNet(include_top=False)
for layer in basic_model.layers:
layers.trainable=False
furtherOutputs = YourOwnLayers()(basic_model.outputs)
You should probably try to copy that final part shown in the keras code, changing classes with your own number of classes. Or maybe try pop 3 layers from the complete model, the Reshape, the Activation and the Conv2D, replacing them with your own.