# In[25]:
from sklearn.linear_model import SGDClassifier
clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=50)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
# In[ ]:
from keras.models import Sequential
from keras import layers
input_dim = X_train.shape[1] # Number of features
model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
history = model.fit(X_train,
y_train,
epochs=100,
verbose=False,
validation_data=(X_test, y_test),
batch_size=10)
loss, accuracy = model.evaluate(X_train, y_train, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))
loss, accuracy = model.evaluate(X_test, y_test, verbose=False)
print("Testing Accuracy: {:.4f}".format(accuracy))
model = NMF(n_components=6, init='random', random_state=0,tol = 5e-3)
W = model.fit_transform(img_train_f)
H = model.components_
matrix = np.dot(W,H)
matrix.shape
X_train = matrix
X_train.resize(1530,224,224,1)
X_train.shape
from keras.layers.normalization import BatchNormalization
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(224,224,1)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#
model.add(Conv2D(64,(3, 3)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(BatchNormalization(axis=-1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#
