This Code reads in a set of testing and training guitar jpg images for the neural net to learn and test from.
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
import random
DATADIR = "C:/Users/TheKid/Data/DataMiningProject/DataSet"
CATEGORIES = ["Fender_Jazzmaster", "Gibson_ES"]
CATEGORIES2 = ["Test"]
for category in CATEGORIES:
path = os.path.join(DATADIR,category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
IMG_SIZE = 70
new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR,category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
training_data.append([new_array,class_num])
create_training_data()
print(len(training_data))
random.shuffle(training_data)
X = []
y = []
for features, label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
for category in CATEGORIES2:
path2 = os.path.join(DATADIR,category)
for img in os.listdir(path2):
img_array2 = cv2.imread(os.path.join(path2,img),cv2.IMREAD_GRAYSCALE)
IMG_SIZE = 70
new_array2 = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
testing_data = []
def create_testing_data():
for category in CATEGORIES2:
path2 = os.path.join(DATADIR,category)
class_num2 = CATEGORIES2.index(category)
for img in os.listdir(path2):
img_array2 = cv2.imread(os.path.join(path2,img),cv2.IMREAD_GRAYSCALE)
new_array2 = cv2.resize(img_array2,(IMG_SIZE,IMG_SIZE))
testing_data.append([new_array2,class_num2])
create_testing_data()
print(len(testing_data))
random.shuffle(testing_data)
X2 = []
y2 = []
for features, label in testing_data:
X2.append(features)
y2.append(label)
X2 = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
import pickle
pickle_out = open("X.pickle" , "wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle" , "wb")
pickle.dump(y, pickle_out)
pickle_out.close()
pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)
pickle_out = open("X2.pickle" , "wb")
pickle.dump(X2, pickle_out)
pickle_out.close()
pickle_out = open("y2.pickle" , "wb")
pickle.dump(y2, pickle_out)
pickle_out.close()
pickle_in = open("X2.pickle", "rb")
X = pickle.load(pickle_in)
This next bit of code takes in the pickle file saved in previous code and is supposed to use Keras tuners search function to run different variants of the neural net with different amounts of conv layer ,layer sizes etc so I can choose the most efficient version. But when run this error gets thrown:
ValueError: Data cardinality is ambiguous:
x sizes: 1312
y sizes: 12
Please provide data which shares the same first dimension.
The Shapes of all the variables are:
(x_train = (1312, 70, 70, 1)
y_train =(1312,)
x_test = (1312, 70, 70, 1)
y_test =(12,)
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import numpy as np
import time
import pickle
import matplotlib.pyplot as plt
from tensorflow import keras
from kerastuner.tuners import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
pickle_in = open("X.pickle","rb")
x_train = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y_train = pickle.load(pickle_in)
pickle_in = open("X2.pickle","rb")
x_test = pickle.load(pickle_in)
pickle_in = open("y2.pickle","rb")
y_test = pickle.load(pickle_in)
x_train=np.array(x_train/255.0)
y_train=np.array(y_train)
x_test=np.array(x_test/255.0)
y_test=np.array(y_test)
LOG_DIR = f"{int(time.time())}"
def build_model(hp):
model = keras.models.Sequential()
model.add(Conv2D(hp.Int("input_units",32, 256, 32 ), (3, 3), input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
for i in range(hp.Int("n_layers", 1, 4)):
model.add(Conv2D(hp.Int(f"conv-{i}_units",32, 256, 32 ), (3, 3)))
model.add(Activation('relu'))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(10))
model.add(Activation("softmax"))
model.compile(optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"])
return model
tuner = RandomSearch(
build_model,
objective = "val_accuracy",
max_trials = 1,
executions_per_trial = 1,
directory = LOG_DIR)
tuner.search(x=x_train,
y=y_train,
epochs=1,
batch_size=64,
validation_data=(x_test,y_test))
with open(f"tuner_{int(time.time())}.pkl", "wb") as f:
pickle.dump(tuner, f)
tuner = pickle.load(open(""))
print(tuner.get_best_hyperparameters()[0].values)
How would I go about resolving this error? It's seems like a matrix formatting issue to me but I have little experience in dealing with a problem like this.
