identity block and convolutional block

Author: chen0040

keras_image_classifier/library/residual_network.py

@@ -0,0 +1,148 @@
+import numpy as np
+from keras import layers
+from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
+from keras.models import Model, load_model
+from keras.preprocessing import image
+from keras.utils import layer_utils
+from keras.utils.data_utils import get_file
+from keras.applications.imagenet_utils import preprocess_input
+from keras.utils.vis_utils import model_to_dot
+from keras.utils import plot_model
+from keras_image_classifier.resnets_utils import *
+from keras.initializers import glorot_uniform
+import scipy.misc
+import keras.backend as K
+import tensorflow as tf
+
+K.set_image_data_format('channels_last')
+K.set_learning_phase(1)
+
+
+def identity_block(X, f, filters, stage, block):
+    """
+    Implementation of the identity block as defined in Figure 3
+
+    Arguments:
+    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
+    f -- integer, specifying the shape of the middle CONV's window for the main path
+    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
+    stage -- integer, used to name the layers, depending on their position in the network
+    block -- string/character, used to name the layers, depending on their position in the network
+
+    Returns:
+    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
+    """
+
+    # 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. You'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 (≈3 lines)
+    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 (≈2 lines)
+    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 (≈2 lines)
+    X = Add()([X, X_shortcut])
+    X = Activation('relu')(X)
+
+    return X
+
+
+def identity_block_test():
+    tf.reset_default_graph()
+
+    with tf.Session() as test:
+        np.random.seed(1)
+        A_prev = tf.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.global_variables_initializer())
+        out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
+        print("out = " + str(out[0][1][1][0]))
+
+
+def convolutional_block(X, f, filters, stage, block, s=2):
+    """
+    Implementation of the convolutional block as defined in Figure 4
+
+    Arguments:
+    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
+    f -- integer, specifying the shape of the middle CONV's window for the main path
+    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
+    stage -- integer, used to name the layers, depending on their position in the network
+    block -- string/character, used to name the layers, depending on their position in the network
+    s -- Integer, specifying the stride to be used
+
+    Returns:
+    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
+    """
+
+    # 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
+
+    # First component of main path
+    X = Conv2D(F1, (1, 1), padding='valid', 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 (≈3 lines)
+    X = Conv2D(F2, (f, f), padding='same', strides=(1, 1), 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 (≈2 lines)
+    X = Conv2D(F3, (1, 1), padding='valid', strides=(1, 1), name=conv_name_base + '2c',
+               kernel_initializer=glorot_uniform(seed=0))(X)
+    X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X)
+
+    ##### SHORTCUT PATH #### (≈2 lines)
+    X_shortcut = Conv2D(F3, (1, 1), padding='valid', 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 (≈2 lines)
+    X = Add()([X, X_shortcut])
+    X = Activation('relu')(X)
+
+    return X
+
+
+def convolutional_block_test():
+    tf.reset_default_graph()
+
+    with tf.Session() as test:
+        np.random.seed(1)
+        A_prev = tf.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.global_variables_initializer())
+        out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
+        print("out = " + str(out[0][1][1][0]))

keras_image_classifier/library/resnet.py

@@ -5,4 +5,4 @@ numpy
 h5py
 pillow
 scikit-learn
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.11.0-cp27-none-linux_x86_64.whl
+tensorflow