# Import Tensorflow 2.0
#%tensorflow_version 2.x --> only google collab
import tensorflow as tf
!pip install mitdeeplearning
import mitdeeplearning as mdl
import matplotlib.pyplot as plt
import numpy as np
import random
from tqdm import tqdm
import cv2
import os
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
import time
import seaborn as sns
import pandas as pd
from sklearn.metrics import confusion_matrix
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D
from keras.models import Model, load_model
from keras.initializers import glorot_uniform
from keras.utils import plot_model
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
import keras.backend as K
Requirement already satisfied: mitdeeplearning in /opt/venv/lib/python3.7/site-packages (0.1.2)
Requirement already satisfied: numpy in /opt/venv/lib/python3.7/site-packages (from mitdeeplearning) (1.18.5)
Requirement already satisfied: regex in /opt/venv/lib/python3.7/site-packages (from mitdeeplearning) (2020.11.13)
Requirement already satisfied: gym in /opt/venv/lib/python3.7/site-packages (from mitdeeplearning) (0.17.3)
Requirement already satisfied: tqdm in /opt/venv/lib/python3.7/site-packages (from mitdeeplearning) (4.54.0)
Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /opt/venv/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.6.0)
Requirement already satisfied: scipy in /opt/venv/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.5.4)
Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /opt/venv/lib/python3.7/site-packages (from gym->mitdeeplearning) (1.5.0)
Requirement already satisfied: future in /opt/venv/lib/python3.7/site-packages (from pyglet<=1.5.0,>=1.4.0->gym->mitdeeplearning) (0.18.2)
WARNING: You are using pip version 20.2.4; however, version 20.3 is available.
You should consider upgrading via the '/opt/venv/bin/python -m pip install --upgrade pip' command.
train_dir = "data/asl_alphabet_train/asl_alphabet_train/"
test_dir = "data/asl_alphabet_test/asl_alphabet_test/"
IMG_SIZE = 64
labels_map = {'A':0,'B':1,'C': 2, 'D': 3, 'E':4,'F':5,'G':6, 'H': 7, 'I':8, 'J':9,'K':10,'L':11, 'M': 12, 'N': 13, 'O':14,
'P':15,'Q':16, 'R': 17, 'S': 18, 'T':19, 'U':20,'V':21, 'W': 22, 'X': 23, 'Y':24, 'Z':25,
'del': 26, 'nothing': 27,'space':28}
classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',
'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
'W', 'X', 'Y', 'Z', 'nothing', 'space', 'del']
plt.figure(figsize=(11, 11))
for i in range (0,29):
plt.subplot(7,7,i+1)
plt.xticks([])
plt.yticks([])
path = train_dir + "/{0}/{0}1.jpg".format(classes[i])
img = plt.imread(path)
plt.imshow(img)
plt.xlabel(classes[i])
#dict_characters=labels_map
#df = pd.DataFrame()
#df["labels"]=y_train
#lab = df['labels']
#dist = lab.value_counts()
#plt.figure(figsize=(12,8))
#sns.countplot(lab)
#print(dict_characters)
def create_train_data():
x_train = []
y_train = []
for folder_name in os.listdir(train_dir):
label = labels_map[folder_name]
for image_filename in tqdm(os.listdir(train_dir + folder_name)):
path = os.path.join(train_dir,folder_name,image_filename)
img = cv2.resize(cv2.imread(path, cv2.IMREAD_COLOR),(IMG_SIZE, IMG_SIZE ))
x_train.append(np.array(img))
y_train.append(np.array(label))
print("Done creating train data")
return x_train, y_train
train_dir
files = [f for f in tqdm(os.listdir(train_dir + 'A'))]
len(files)
files[0]
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'''def create_train_data():
x_train = []
y_train = []
for folder_name in os.listdir(train_dir):
label = labels_map[folder_name]
files = [f for f in tqdm(os.listdir(train_dir + folder_name))]
for i in range(0,15):
#for image_filename in tqdm(os.listdir(train_dir + folder_name)):
path = os.path.join(train_dir,files[i])
img = cv2.resize(cv2.imread(path, cv2.IMREAD_COLOR),(IMG_SIZE, IMG_SIZE ))
x_train.append(np.array(img))
y_train.append(np.array(label))
print("Done creating train data")
return x_train, y_train
'''
def create_test_data():
x_test = []
y_test = []
for folder_name in os.listdir(test_dir):
label = labels_map[folder_name]
for image_filename in tqdm(os.listdir(test_dir + folder_name)):
path = os.path.join(test_dir,folder_name,image_filename)
img = cv2.resize(cv2.imread(path, cv2.IMREAD_COLOR),(IMG_SIZE, IMG_SIZE ))
x_test.append(np.array(img))
y_test.append(np.array(label))
print("Done creating test data")
return x_test,y_test
x_train, y_train= create_train_data()
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45%|████▌ | 1364/3000 [00:03<00:03, 456.12it/s]x_test,y_test = create_test_data()
#num_features = 2500
#num_classes = 29
#x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)
#x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])
#x_train, x_test = x_train / 255., x_test / 255.
x_train = x_train[0:1000]
y_train = y_train[0:1000]
y_train[0]
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)].T
return Y
y_train_hot = convert_to_one_hot(np.array(y_train), 29).T
y_test_hot = convert_to_one_hot(np.array(y_test), 29).T
'''x_train = (np.expand_dims(x_train, axis=-1)/255.).astype(np.float32)
x_test = (np.expand_dims(x_test, axis=-1)/255.).astype(np.float32)
y_train = np.array(y_train, dtype=np.int64)
y_test = np.array(y_test, dtype=np.int64)'''
def identity_block(X, f, filters, stage, block):
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value. We'll need this later to add back to the main path.
X_shortcut = X
# First component of main path
X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
# Second component of main path
X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
X = Activation('relu')(X)
# Third component of main path
X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)
# Final step: Add shortcut value to main path, and pass it through a RELU activation
X = Add()([X, X_shortcut])
X = Activation('relu')(X)
return X
# tf.compat.v1 instead of tf as functions are outdated
tf.compat.v1.reset_default_graph()
with tf.compat.v1.Session() as test:
A_prev = tf.compat.v1.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.compat.v1.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = ", out[0][1][1][0])
out = [0.5655951 0.7026807 0. 0.8893434 0. 1.6481289]
def convolutional_block(X, f, filters, stage, block, s = 2):
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value
X_shortcut = X
##### MAIN PATH #####
# First component of main path
X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
# Second component of main path
X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X)
X = Activation('relu')(X)
# Third component of main path
X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)
##### SHORTCUT PATH ####
X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '1', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)
X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)
# Final step: Add shortcut value to main path, and pass it through a RELU activation
X = Add()([X, X_shortcut])
X = Activation('relu')(X)
return X
tf.compat.v1.reset_default_graph()
with tf.compat.v1.Session() as test:
A_prev = tf.compat.v1.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.compat.v1.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = ",out[0][1][1][0])
out = [1.758054 0. 0.99052036 0. 0.6673316 0. ]
def ResNet50(input_shape = (64, 64, 3), classes = 29):
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')
# Stage 3
X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)
X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')
# Stage 4
X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')
# Stage 5
X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)
X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')
X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')
# AVGPOOL.
X = AveragePooling2D((2, 2), name='avg_pool')(X)
# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs = X_input, outputs = X, name='ResNet50')
return model
ROWS = 64
COLS = 64
CHANNELS = 3
CLASSES = 29
# build the model's graph
model = ResNet50(input_shape =(ROWS, COLS, CHANNELS), classes = CLASSES)
#Now we need to configure the learning process by compiling the model.
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#The model is now ready to be trained. Run the following cell to train your model on 100 epochs with a batch size of 64:
model.fit(x_train, y_train_hot, epochs = 100, batch_size = 64)
ValueError: Data cardinality is ambiguous:
x sizes: 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64
y sizes: 1000
Please provide data which shares the same first dimension.
preds = model.evaluate(X_test, Y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))
model.summary()
plot_model(model, to_file='model.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))
%matplotlib inline
import matplotlib.pyplot as plt
def display_image(num):
label = y_train[num]
plt.title('Label: %d' % (label))
image = x_train[num].reshape([IMG_SIZE,IMG_SIZE])
plt.imshow(image, cmap=plt.get_cmap('gray_r'))
plt.show()
display_image(1000)
def build_fc_model():
fc_model = tf.keras.Sequential([
# First define a Flatten layer
tf.keras.layers.Flatten(),
# '''TODO: Define the activation function for the first fully connected (Dense) layer.'''
tf.keras.layers.Dense(128, activation= 'relu'),
# '''TODO: Define the second Dense layer to output the classification probabilities'''
tf.keras.layers.Dense(29, activation= 'softmax')
])
return fc_model
model_sgd = build_fc_model()
'''TODO: Experiment with different optimizers and learning rates. How do these affect
the accuracy of the trained model? Which optimizers and/or learning rates yield
the best performance?'''
model_sgd.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=1e-1),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Define the batch size and the number of epochs to use during training
BATCH_SIZE = 64
EPOCHS = 5
model_sgd.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS)
RuntimeError: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`.
print(model_sgd.summary())
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten_4 (Flatten) (None, 784) 0
_________________________________________________________________
dense_8 (Dense) (None, 128) 100480
_________________________________________________________________
dense_9 (Dense) (None, 29) 3741
=================================================================
Total params: 104,221
Trainable params: 104,221
Non-trainable params: 0
_________________________________________________________________
None
'''TODO: Use the evaluate method to test the model!'''
test_loss, test_acc = model_sgd.evaluate(x= x_test, y=y_test)
'''TODO: Use the evaluate method to test the model!'''
train_loss, train_acc = model_sgd.evaluate(x= x_train, y=y_train)
print('Test accuracy:', test_acc)
print('Train accuracy:', train_acc)
1/1 [==============================] - 0s 1ms/step - loss: 0.5430 - accuracy: 0.7931
2719/2719 [==============================] - 3s 1ms/step - loss: 1.0845 - accuracy: 0.6290
Test accuracy: 0.7931034564971924
Train accuracy: 0.62898850440979
def build_classifier(optimizer = 'adam', learning_rate=1e-1):
classifier = build_fc_model()
if optimizer == 'SGD':
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
if optimizer == 'RMSprop':
optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
if optimizer == 'Adagrad':
optimizer = tf.keras.optimizers.Adagrad(learning_rate=learning_rate)
if optimizer == 'Adadelta':
optimizer = tf.keras.optimizers.Adadelta(learning_rate=learning_rate)
if optimizer == 'Adam':
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
if optimizer == 'Adamax':
optimizer = tf.keras.optimizers.Adamax(learning_rate=learning_rate)
if optimizer == 'Nadam':
optimizer = tf.keras.optimizers.Nadam(learning_rate=learning_rate)
classifier.compile(#optimizer=tf.keras.optimizers.SGD(learning_rate=1e-1),
optimizer = optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return classifier
optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
learning_rate = [0.001, 0.01, 0.1, 0.2, 0.3]
param_grid = dict(optimizer=optimizer, learning_rate = learning_rate)
# create model
my_wrapper_model = KerasClassifier(build_fn=build_classifier, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=0)
grid = GridSearchCV(estimator=my_wrapper_model, param_grid=param_grid, n_jobs=-1, cv=3, scoring='accuracy')
start_time = time.time()
grid_result = grid.fit(x_train, y_train)
print(f'--- {(time.time() - start_time)/60:.3f} minutes ---')
--- 20.245 minutes ---
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
#for mean, stdev, param in zip(means, stds, params):
#print("%f (%f) with: %r" % (mean, stdev, param))
Best: 0.022437 using {'learning_rate': 0.001, 'optimizer': 'Nadam'}
model_nadam = build_fc_model()
model_nadam.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate=0.001),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model_nadam.fit(x_test, y_test, batch_size=BATCH_SIZE, epochs=EPOCHS)
'''TODO: Use the evaluate method to test the model!'''
test_loss, test_acc = model_nadam.evaluate(x= x_test, y=y_test)
print('Test accuracy:', test_acc)
Epoch 1/5
1/1 [==============================] - 0s 1ms/step - loss: 3.6106 - accuracy: 0.0357
Epoch 2/5
1/1 [==============================] - 0s 813us/step - loss: 3.3604 - accuracy: 0.0357
Epoch 3/5
1/1 [==============================] - 0s 755us/step - loss: 3.2048 - accuracy: 0.0357
Epoch 4/5
1/1 [==============================] - 0s 824us/step - loss: 3.0951 - accuracy: 0.2857
Epoch 5/5
1/1 [==============================] - 0s 800us/step - loss: 2.9977 - accuracy: 0.3214
WARNING:tensorflow:8 out of the last 5449 calls to <function Model.make_test_function.<locals>.test_function at 0x7f86e85b5ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.
1/1 [==============================] - 0s 976us/step - loss: 2.9069 - accuracy: 0.5000
Test accuracy: 0.5
def build_cnn_model():
cnn_model = tf.keras.Sequential([
# TODO: Define the first convolutional layer
# 24: filter size
# 3: Kernel Size: height and width of the 2D convolution (3)
tf.keras.layers.Conv2D(24, 3, input_shape = (28, 28, 1), activation=tf.nn.relu),
# TODO: Define the first max pooling layer
tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2),
# TODO: Define the second convolutional layer
tf.keras.layers.Conv2D(36, 3, activation=tf.nn.relu),
# TODO: Define the second max pooling layer
tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
# TODO: Define the last Dense layer to output the classification
# probabilities. Pay attention to the activation needed for a probability
# output
#'''TODO: Dense layer to output classification probabilities'''
tf.keras.layers.Dense(29, activation= 'softmax')
])
return cnn_model
cnn_model_nadam = build_cnn_model()
# Initialize the model by passing some data through
cnn_model_nadam.predict(x_train[[0]])
# Print the summary of the layers in the model.
print(cnn_model_nadam.summary())
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 24) 240
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 24) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 36) 7812
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 36) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 900) 0
_________________________________________________________________
dense_2 (Dense) (None, 128) 115328
_________________________________________________________________
dense_3 (Dense) (None, 29) 3741
=================================================================
Total params: 127,121
Trainable params: 127,121
Non-trainable params: 0
_________________________________________________________________
None
'''TODO: Define the compile operation with your optimizer and learning rate of choice'''
cnn_model_nadam.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
'''TODO: Use model.fit to train the CNN model, with the same batch_size and number of epochs previously used.'''
history = cnn_model_nadam.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs = EPOCHS, verbose = 1, validation_data = (x_test, y_test))
Epoch 1/5
2719/2719 [==============================] - 94s 35ms/step - loss: 3.3675 - accuracy: 0.0334 - val_loss: 3.3746 - val_accuracy: 0.0345
Epoch 2/5
2719/2719 [==============================] - 93s 34ms/step - loss: 3.3675 - accuracy: 0.0340 - val_loss: 3.3751 - val_accuracy: 0.0345
Epoch 3/5
2719/2719 [==============================] - 93s 34ms/step - loss: 3.3675 - accuracy: 0.0329 - val_loss: 3.3754 - val_accuracy: 0.0345
Epoch 4/5
2719/2719 [==============================] - 95s 35ms/step - loss: 3.3675 - accuracy: 0.0343 - val_loss: 3.3754 - val_accuracy: 0.0345
Epoch 5/5
2719/2719 [==============================] - 95s 35ms/step - loss: 3.3675 - accuracy: 0.0335 - val_loss: 3.3753 - val_accuracy: 0.0345
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.plot(history.history['loss'], label='Loss')
plt.plot(history.history['val_loss'], label='val_Loss')
plt.legend()
plt.grid()
plt.title('Loss evolution')
plt.subplot(2, 2, 2)
plt.plot(history.history['accu