I use Keras tuner for hyperparameter tuning on digit recognizer datasets but got error
first I made build method in CNNHyperModel class for hyper parameter tuning
second I use Conv2D , MaxPooling2D, Dropout then neural network
I already imported libraries which i required for this program
class CNNHyperModel(HyperModel):
#def __init__(self, input_shape, num_classes):
#self.input_shape =input_shape
#self.num_classes =num_classes
def build(self,hp) :
model=keras.Sequential()
model.add( Conv2D(filters=hp.Choice('1Conv_num_classes',
values=[32,64,128,256]),
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
)
model.add(Conv2D(filters=hp.Choice("2Conv_num_classes",
values=[32,54,128,256]),
activation='relu',strides=1,padding='same',kernal_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=hp.Float("1Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Conv2D(filters=hp.Choice("3Conv_num_classes",
values=[32,64,128,256]),
activation='relu',strides=1,padding='same',kernal_size=(3,3)))
model.add(Conv2D(filters=hp.Choice("4Conv_num_classes",
values=[32,64,128,256]),
activation='relu',strides=1,padding='same',kernal_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(DropOut(rate=hp.Float("2Dropout", min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Conv2d(filters=hp.Choice("5Conv_num_classes",
values=[32,64,128,256]),
activation='relu',strides=1,padding='same',kernal_size=(3,3)))
model.add(Conv2D(filters=hp.Choice("6Conv_NUM_CLASSES",
values=[32,64,128,256]),
activation='relu',strides=1,padding='same',kernal_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Dropout(rate=hp.Float("3Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Flatten())
model.add(Dense(units=hp.Int("Dense",min_value=32,
max_value=512,step=32),activation='relu'))
model.add(Dropout(rate=hp.Float("Dense_Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Dense(units=hp.Int("2Dense",min_values=32,
max_values=512,step=32),activation='relu'))
model.add(Dropout(rate=hp.Float("2Dense_Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Dense(10,activation='sigmoid'))
"""model.compile(optimizer=keras.optimizers.Adam(
hp.Float(
"Learning_rate",
min_value=le-4,
max_value=le-2,
sampling="LOG"
)
),"""
model.compie(optimizer="sgd",loss="sparse_categorical_crossentropy",metrics=['accuracy'])
return model
#hypermodel=CNNHyperModel((28,28,1),10)
hypermodel=CNNHyperModel()
tuner = RandomSearch(
hypermodel,
objective='accuracy',
max_trials=15,executions_per_trial=3,directory='my_dir',
project_name='digit'
)
But i got RuntimeError
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
Invalid model 0/5
Invalid model 1/5
Invalid model 2/5
Invalid model 3/5
Invalid model 4/5
Invalid model 5/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 104, in build
model = self.hypermodel.build(hp)
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 64, in _build_wrapper
return self._build(hp, *args, **kwargs)
File "<ipython-input-17-9b2a20a37331>", line 10, in build
activation="relu",strides=1,padding='same', kernal_size=(3,3),input_shape=(28,28,1))
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
103 with maybe_distribute(self.distribution_strategy):
--> 104 model = self.hypermodel.build(hp)
105 except:
9 frames
TypeError: __init__() missing 1 required positional argument: 'kernel_size'
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
111 if i == self._max_fail_streak:
112 raise RuntimeError(
--> 113 'Too many failed attempts to build model.')
114 continue
115
RuntimeError: Too many failed attempts to build model.
In above code there is some spelling error and improvemt require
spelling mistake like kernal_size ->kernel_size
so here is working core with same improvement
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.layers import ( Conv2D ,MaxPooling2D,
Dropout,Dense,Flatten)
from kerastuner.tuners import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
from kerastuner import HyperModel
import pandas as pd
import numpy as np
class CNNHyperModel(HyperModel):
#def __init__(self, input_shape, num_classes):
#self.input_shape =input_shape
#self.num_classes =num_classes
def build(self,hp) :
model=keras.Sequential()
model.add( Conv2D(filters=hp.Int('1Conv_num_classes',default=32,min_value=32,step=16,
max_value=256),
activation="relu",strides=1,padding='same', kernel_size=(3,3),input_shape=(28,28,1))
)
model.add(Conv2D(filters=hp.Int("2Conv_num_classes",default=32,min_value=32,
max_value=256,step=16),
activation='relu',strides=1,padding='same',kernel_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=hp.Float("1Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Conv2D(filters=hp.Int("3Conv_num_classes",default=64,min_value=32,
max_value=256,step=16),
activation='relu',strides=1,padding='same',kernel_size=(3,3)))
model.add(Conv2D(filters=hp.Int("4Conv_num_classes",default=64,min_value=32,
max_value=256,step=16),
activation='relu',strides=1,padding='same',kernel_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Dropout(rate=hp.Float("2Dropout", min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Conv2D(filters=hp.Int("5Conv_num_classes",default=128,min_value=32,
max_value=256,step=16),
activation='relu',strides=1,padding='same',kernel_size=(3,3)))
model.add(Conv2D(filters=hp.Int("6Conv_NUM_CLASSES",default=128,min_value=32,
max_value=256,step=16),
activation='relu',strides=1,padding='same',kernel_size=(3,3)))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Dropout(rate=hp.Float("3Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Flatten())
model.add(Dense(units=hp.Int("Dense",min_value=32,default=516,
max_value=512,step=16),activation='relu'))
model.add(Dropout(rate=hp.Float("Dense_Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Dense(units=hp.Int("2Dense",min_value=32,default=516,
max_value=512,step=16),activation='relu'))
model.add(Dropout(rate=hp.Float("2Dense_Dropout",min_value=0.0,
max_value=0.5,step=0.05)))
model.add(Dense(10,activation='sigmoid'))
"""model.compile(optimizer=keras.optimizers.Adam(
hp.Float(
"Learning_rate",
min_value=le-4,
max_value=le-2,
sampling="LOG"
),loss="sparse_categorical_crossentropy",metrics=['accuracy'])
),"""
model.compile(optimizer="sgd",loss="sparse_categorical_crossentropy",metrics=['accuracy'])
return model
#hypermodel=CNNHyperModel((28,28,1),10)
hypermodel=CNNHyperModel()
as you see I pass strides=1,padding='same' in Conv2D for more optimization
happy coding